diff --git a/data/evaluations/Scene6/aruco_marker_poses.json b/data/evaluations/Scene6/aruco_marker_poses.json
index 26bd2db..e247771 100644
--- a/data/evaluations/Scene6/aruco_marker_poses.json
+++ b/data/evaluations/Scene6/aruco_marker_poses.json
@@ -1,7 +1,7 @@
 {
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+  "created_utc": "2026-06-02T06:13:00Z",
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diff --git a/data/evaluations/Scene6/aruco_positions_initial.json b/data/evaluations/Scene6/aruco_positions_initial.json
index c32efa7..06c0dbc 100644
--- a/data/evaluations/Scene6/aruco_positions_initial.json
+++ b/data/evaluations/Scene6/aruco_positions_initial.json
@@ -1,7 +1,7 @@
 {
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diff --git a/data/evaluations/Scene6/aruco_positions_optimized.json b/data/evaluations/Scene6/aruco_positions_optimized.json
index 346891e..88b8e07 100644
--- a/data/evaluations/Scene6/aruco_positions_optimized.json
+++ b/data/evaluations/Scene6/aruco_positions_optimized.json
@@ -1,6 +1,6 @@
 {
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+  "created_utc": "2026-06-02T06:13:00Z",
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diff --git a/data/evaluations/Scene6/render_a_aruco_detection.json b/data/evaluations/Scene6/render_a_aruco_detection.json
index 0f81305..44d6109 100644
--- a/data/evaluations/Scene6/render_a_aruco_detection.json
+++ b/data/evaluations/Scene6/render_a_aruco_detection.json
@@ -1,6 +1,6 @@
 {
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+  "created_utc": "2026-06-02T06:12:43Z",
   "vision_config": {
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@@ -46,7 +46,7 @@
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diff --git a/data/evaluations/Scene6/render_a_camera_pose.json b/data/evaluations/Scene6/render_a_camera_pose.json
index d5aa3dc..5c548eb 100644
--- a/data/evaluations/Scene6/render_a_camera_pose.json
+++ b/data/evaluations/Scene6/render_a_camera_pose.json
@@ -1,6 +1,6 @@
 {
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-  "created_utc": "2026-06-02T05:04:50Z",
+  "created_utc": "2026-06-02T06:12:45Z",
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     "robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json"
diff --git a/data/evaluations/Scene6/render_b_aruco_detection.json b/data/evaluations/Scene6/render_b_aruco_detection.json
index c6ded67..66ab684 100644
--- a/data/evaluations/Scene6/render_b_aruco_detection.json
+++ b/data/evaluations/Scene6/render_b_aruco_detection.json
@@ -1,6 +1,6 @@
 {
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@@ -46,7 +46,7 @@
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diff --git a/data/evaluations/Scene6/render_b_camera_pose.json b/data/evaluations/Scene6/render_b_camera_pose.json
index 642a073..c0bf9de 100644
--- a/data/evaluations/Scene6/render_b_camera_pose.json
+++ b/data/evaluations/Scene6/render_b_camera_pose.json
@@ -1,6 +1,6 @@
 {
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diff --git a/data/evaluations/Scene6/render_c_camera_pose.json b/data/evaluations/Scene6/render_c_camera_pose.json
index 2541fbe..4bffe7d 100644
--- a/data/evaluations/Scene6/render_c_camera_pose.json
+++ b/data/evaluations/Scene6/render_c_camera_pose.json
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diff --git a/data/evaluations/Scene6/render_d_camera_pose.json b/data/evaluations/Scene6/render_d_camera_pose.json
index 5b107a0..035a56a 100644
--- a/data/evaluations/Scene6/render_d_camera_pose.json
+++ b/data/evaluations/Scene6/render_d_camera_pose.json
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diff --git a/data/evaluations/Scene6/render_e_camera_pose.json b/data/evaluations/Scene6/render_e_camera_pose.json
index 55acd3d..5029c23 100644
--- a/data/evaluations/Scene6/render_e_camera_pose.json
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diff --git a/data/evaluations/Scene6/render_f_aruco_detection.json b/data/evaluations/Scene6/render_f_aruco_detection.json
index f718b44..219a9c9 100644
--- a/data/evaluations/Scene6/render_f_aruco_detection.json
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diff --git a/data/evaluations/Scene6/render_f_camera_pose.json b/data/evaluations/Scene6/render_f_camera_pose.json
index 179ff7e..084d567 100644
--- a/data/evaluations/Scene6/render_f_camera_pose.json
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 {
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diff --git a/data/evaluations/Scene6/render_g_aruco_detection.json b/data/evaluations/Scene6/render_g_aruco_detection.json
index 92f42d5..50bf10b 100644
--- a/data/evaluations/Scene6/render_g_aruco_detection.json
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diff --git a/data/evaluations/Scene6/render_g_camera_pose.json b/data/evaluations/Scene6/render_g_camera_pose.json
index 6d99c3a..1ccb7ca 100644
--- a/data/evaluations/Scene6/render_g_camera_pose.json
+++ b/data/evaluations/Scene6/render_g_camera_pose.json
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 {
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diff --git a/data/evaluations/Scene6/robot_state.json b/data/evaluations/Scene6/robot_state.json
index 0156ba3..55d0a42 100644
--- a/data/evaluations/Scene6/robot_state.json
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@@ -1,6 +1,6 @@
 {
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diff --git a/data/evaluations/Scene8/aruco_marker_poses.json b/data/evaluations/Scene8/aruco_marker_poses.json
index c61e334..b4fee0f 100644
--- a/data/evaluations/Scene8/aruco_marker_poses.json
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 }
\ No newline at end of file
diff --git a/data/evaluations/Scene8/aruco_positions_initial.json b/data/evaluations/Scene8/aruco_positions_initial.json
index ee18535..e123851 100644
--- a/data/evaluations/Scene8/aruco_positions_initial.json
+++ b/data/evaluations/Scene8/aruco_positions_initial.json
@@ -1,152 +1,171 @@
 {
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@@ -155,36 +174,17 @@
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diff --git a/data/evaluations/Scene8/aruco_positions_optimized.json b/data/evaluations/Scene8/aruco_positions_optimized.json
index ebbe350..01e7254 100644
--- a/data/evaluations/Scene8/aruco_positions_optimized.json
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index a85e517..97b39e5 100644
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diff --git a/data/evaluations/Scene8/render_a_camera_pose.json b/data/evaluations/Scene8/render_a_camera_pose.json
index c2a5f91..bf17959 100644
--- a/data/evaluations/Scene8/render_a_camera_pose.json
+++ b/data/evaluations/Scene8/render_a_camera_pose.json
@@ -1,6 +1,6 @@
 {
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+  "created_utc": "2026-06-02T09:26:17Z",
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diff --git a/data/evaluations/Scene8/render_b_aruco_detection.json b/data/evaluations/Scene8/render_b_aruco_detection.json
index 02f85be..289a0e9 100644
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diff --git a/data/evaluations/Scene8/render_b_camera_pose.json b/data/evaluations/Scene8/render_b_camera_pose.json
index 97fc6db..ab4809a 100644
--- a/data/evaluations/Scene8/render_b_camera_pose.json
+++ b/data/evaluations/Scene8/render_b_camera_pose.json
@@ -1,6 +1,6 @@
 {
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+  "created_utc": "2026-06-02T09:26:17Z",
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diff --git a/data/evaluations/Scene8/render_c_aruco_detection.json b/data/evaluations/Scene8/render_c_aruco_detection.json
index 1ec8a01..e7e36c5 100644
--- a/data/evaluations/Scene8/render_c_aruco_detection.json
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\ No newline at end of file
diff --git a/data/evaluations/Scene8/render_c_camera_pose.json b/data/evaluations/Scene8/render_c_camera_pose.json
index e96363d..b2031d1 100644
--- a/data/evaluations/Scene8/render_c_camera_pose.json
+++ b/data/evaluations/Scene8/render_c_camera_pose.json
@@ -1,6 +1,6 @@
 {
   "schema_version": "1.0",
-  "created_utc": "2026-06-02T05:06:54Z",
+  "created_utc": "2026-06-02T09:26:18Z",
   "source": {
     "detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene8\\render_c_aruco_detection.json",
     "robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json"
@@ -9,14 +9,14 @@
     "camera_id": "c",
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@@ -25,8 +25,8 @@
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-      0.0,
+      0.019999999552965164,
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@@ -36,218 +36,252 @@
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     "marker_size_m": 0.025,
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+    "num_used_markers": 6,
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diff --git a/data/evaluations/Scene8/render_d_aruco_detection.json b/data/evaluations/Scene8/render_d_aruco_detection.json
index a6d6d57..dacf2e2 100644
--- a/data/evaluations/Scene8/render_d_aruco_detection.json
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diff --git a/data/evaluations/Scene8/render_d_camera_pose.json b/data/evaluations/Scene8/render_d_camera_pose.json
index 5a758a7..59e458f 100644
--- a/data/evaluations/Scene8/render_d_camera_pose.json
+++ b/data/evaluations/Scene8/render_d_camera_pose.json
@@ -1,6 +1,6 @@
 {
   "schema_version": "1.0",
-  "created_utc": "2026-06-02T05:06:54Z",
+  "created_utc": "2026-06-02T09:26:18Z",
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     "robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json"
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@@ -36,17 +36,21 @@
     "method": "single_camera_marker_center_lm",
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     "marker_size_m": 0.025,
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diff --git a/data/evaluations/Scene8/render_e_aruco_detection.json b/data/evaluations/Scene8/render_e_aruco_detection.json
index 32fb07b..2b7e873 100644
--- a/data/evaluations/Scene8/render_e_aruco_detection.json
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 }
\ No newline at end of file
diff --git a/data/evaluations/Scene8/render_e_camera_pose.json b/data/evaluations/Scene8/render_e_camera_pose.json
index a47534c..24773a1 100644
--- a/data/evaluations/Scene8/render_e_camera_pose.json
+++ b/data/evaluations/Scene8/render_e_camera_pose.json
@@ -1,6 +1,6 @@
 {
   "schema_version": "1.0",
-  "created_utc": "2026-06-02T05:06:55Z",
+  "created_utc": "2026-06-02T09:26:18Z",
   "source": {
     "detection_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\evaluations\\Scene8\\render_e_aruco_detection.json",
     "robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json"
@@ -9,14 +9,14 @@
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@@ -25,8 +25,8 @@
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+      0.019999999552965164,
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@@ -36,32 +36,29 @@
     "method": "single_camera_marker_center_lm",
     "description": "Rigid init from per-marker pose estimates, followed by LM on normalized marker-center reprojection residuals.",
     "marker_size_m": 0.025,
-    "num_used_markers": 24,
+    "num_used_markers": 21,
     "used_marker_ids": [
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@@ -71,10 +68,10 @@
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diff --git a/data/evaluations/Scene8/render_f_aruco_detection.json b/data/evaluations/Scene8/render_f_aruco_detection.json
index 4f6fd6e..e51e6c7 100644
--- a/data/evaluations/Scene8/render_f_aruco_detection.json
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diff --git a/data/evaluations/Scene8/render_f_camera_pose.json b/data/evaluations/Scene8/render_f_camera_pose.json
index 1aa8ce7..74c52be 100644
--- a/data/evaluations/Scene8/render_f_camera_pose.json
+++ b/data/evaluations/Scene8/render_f_camera_pose.json
@@ -1,6 +1,6 @@
 {
   "schema_version": "1.0",
-  "created_utc": "2026-06-02T05:06:55Z",
+  "created_utc": "2026-06-02T09:26:19Z",
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     "robot_json": "C:\\Users\\kech\\SynologyDrive\\2026-AppServer-AppRobot\\appRobotRendering\\data\\robot\\robot.json"
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     "marker_size_m": 0.025,
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diff --git a/data/evaluations/Scene8/render_g_aruco_detection.json b/data/evaluations/Scene8/render_g_aruco_detection.json
index bca1dfb..a542650 100644
--- a/data/evaluations/Scene8/render_g_aruco_detection.json
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diff --git a/data/evaluations/Scene8/render_g_camera_pose.json b/data/evaluations/Scene8/render_g_camera_pose.json
index bee21ad..705842e 100644
--- a/data/evaluations/Scene8/render_g_camera_pose.json
+++ b/data/evaluations/Scene8/render_g_camera_pose.json
@@ -1,6 +1,6 @@
 {
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diff --git a/data/evaluations/Scene8/robot_state.json b/data/evaluations/Scene8/robot_state.json
index 7e8b97f..906dfdd 100644
--- a/data/evaluations/Scene8/robot_state.json
+++ b/data/evaluations/Scene8/robot_state.json
@@ -1,58 +1,58 @@
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       "observable": true,
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-  "num_markers": 56
+  "residual_rms": 2.1752805972816693,
+  "num_markers": 54
 }
\ No newline at end of file
diff --git a/data/robot/robot.json b/data/robot/robot.json
index 531d294..c46a174 100644
--- a/data/robot/robot.json
+++ b/data/robot/robot.json
@@ -99,6 +99,12 @@
     "markerRotationMaxDeg": 3,
     "motionBlur": true,
     "motionBlurMaxPx": 5.5,
+    "focalErrorPct": 0.5,
+    "principalErrorPx": 3.0,
+    "residualDistortion": [
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     "localizedBlurStrength": 0.15,
     "vignette": true,
@@ -209,13 +215,13 @@
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-    "y": 86,
-    "z": -120,
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-    "b": 22,
-    "c": 91,
-    "e": 10
+    "x": 50,
+    "y": -2,
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+    "a": 20,
+    "b": 60,
+    "c": 9,
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diff --git a/data/simulation/Scene6/pose.json b/data/simulation/Scene6/pose.json
index c25c898..2439012 100644
--- a/data/simulation/Scene6/pose.json
+++ b/data/simulation/Scene6/pose.json
@@ -10,8 +10,8 @@
     "e": 3
   },
   "rendering": {
-    "width": 1280,
-    "height": 720,
+    "width": 1440,
+    "height": 1080,
     "dofFStop": 11.0
   },
   "camera_positions": {
diff --git a/data/simulation/Scene6/render_a.json b/data/simulation/Scene6/render_a.json
index ca2dfe5..881500a 100644
--- a/data/simulation/Scene6/render_a.json
+++ b/data/simulation/Scene6/render_a.json
@@ -4,20 +4,20 @@
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@@ -30,20 +30,20 @@
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@@ -160,20 +160,20 @@
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@@ -264,20 +264,20 @@
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\ No newline at end of file
diff --git a/data/simulation/Scene6/render_a.npz b/data/simulation/Scene6/render_a.npz
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diff --git a/data/simulation/Scene6/render_a.png b/data/simulation/Scene6/render_a.png
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index ca2dfe5..881500a 100644
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\ No newline at end of file
diff --git a/data/simulation/Scene6/render_b.npz b/data/simulation/Scene6/render_b.npz
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diff --git a/data/simulation/Scene6/render_b.png b/data/simulation/Scene6/render_b.png
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diff --git a/data/simulation/Scene6/render_c.json b/data/simulation/Scene6/render_c.json
new file mode 100644
index 0000000..881500a
--- /dev/null
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\ No newline at end of file
diff --git a/data/simulation/Scene6/render_c.npz b/data/simulation/Scene6/render_c.npz
new file mode 100644
index 0000000..5a60970
Binary files /dev/null and b/data/simulation/Scene6/render_c.npz differ
diff --git a/data/simulation/Scene6/render_c.png b/data/simulation/Scene6/render_c.png
new file mode 100644
index 0000000..f521f5a
Binary files /dev/null and b/data/simulation/Scene6/render_c.png differ
diff --git a/data/simulation/Scene6/render_d.json b/data/simulation/Scene6/render_d.json
new file mode 100644
index 0000000..881500a
--- /dev/null
+++ b/data/simulation/Scene6/render_d.json
@@ -0,0 +1,2446 @@
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\ No newline at end of file
diff --git a/data/simulation/Scene6/render_d.npz b/data/simulation/Scene6/render_d.npz
new file mode 100644
index 0000000..5a60970
Binary files /dev/null and b/data/simulation/Scene6/render_d.npz differ
diff --git a/data/simulation/Scene6/render_d.png b/data/simulation/Scene6/render_d.png
new file mode 100644
index 0000000..90ec4ea
Binary files /dev/null and b/data/simulation/Scene6/render_d.png differ
diff --git a/data/simulation/Scene6/render_e.json b/data/simulation/Scene6/render_e.json
new file mode 100644
index 0000000..881500a
--- /dev/null
+++ b/data/simulation/Scene6/render_e.json
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+  }
+]
\ No newline at end of file
diff --git a/data/simulation/Scene6/render_e.npz b/data/simulation/Scene6/render_e.npz
new file mode 100644
index 0000000..5a60970
Binary files /dev/null and b/data/simulation/Scene6/render_e.npz differ
diff --git a/data/simulation/Scene6/render_e.png b/data/simulation/Scene6/render_e.png
new file mode 100644
index 0000000..79386be
Binary files /dev/null and b/data/simulation/Scene6/render_e.png differ
diff --git a/data/simulation/Scene6/render_f.json b/data/simulation/Scene6/render_f.json
index ca2dfe5..881500a 100644
--- a/data/simulation/Scene6/render_f.json
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\ No newline at end of file
diff --git a/data/simulation/Scene6/render_f.npz b/data/simulation/Scene6/render_f.npz
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diff --git a/data/simulation/Scene6/render_f.png b/data/simulation/Scene6/render_f.png
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\ No newline at end of file
diff --git a/data/simulation/Scene6/render_g.npz b/data/simulation/Scene6/render_g.npz
index b10ee26..5a60970 100644
Binary files a/data/simulation/Scene6/render_g.npz and b/data/simulation/Scene6/render_g.npz differ
diff --git a/data/simulation/Scene6/render_g.png b/data/simulation/Scene6/render_g.png
index ca5be3b..205da35 100644
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diff --git a/data/simulation/Scene8/pose.json b/data/simulation/Scene8/pose.json
index 0025639..91eb06b 100644
--- a/data/simulation/Scene8/pose.json
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@@ -10,8 +10,8 @@
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diff --git a/data/simulation/Scene8/render_a.json b/data/simulation/Scene8/render_a.json
index 81b2b95..685caa0 100644
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\ No newline at end of file
diff --git a/data/simulation/Scene8/render_a.npz b/data/simulation/Scene8/render_a.npz
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diff --git a/data/simulation/Scene8/render_a.png b/data/simulation/Scene8/render_a.png
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diff --git a/data/simulation/Scene8/render_b.json b/data/simulation/Scene8/render_b.json
index 81b2b95..685caa0 100644
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\ No newline at end of file
diff --git a/data/simulation/Scene8/render_b.npz b/data/simulation/Scene8/render_b.npz
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diff --git a/data/simulation/Scene8/render_b.png b/data/simulation/Scene8/render_b.png
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diff --git a/data/simulation/Scene8/render_c.json b/data/simulation/Scene8/render_c.json
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\ No newline at end of file
diff --git a/data/simulation/Scene8/render_c.npz b/data/simulation/Scene8/render_c.npz
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diff --git a/data/simulation/Scene8/render_c.png b/data/simulation/Scene8/render_c.png
index ed21d82..321788d 100644
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diff --git a/data/simulation/Scene8/render_e.npz b/data/simulation/Scene8/render_e.npz
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diff --git a/data/simulation/debug/render.npz b/data/simulation/debug/render.npz
index 5a60970..c4959f7 100644
Binary files a/data/simulation/debug/render.npz and b/data/simulation/debug/render.npz differ
diff --git a/data/simulation/debug/render.png b/data/simulation/debug/render.png
index 69685d0..71699c3 100644
Binary files a/data/simulation/debug/render.png and b/data/simulation/debug/render.png differ
diff --git a/doc/camera_number_roadmap.md b/doc/camera_number_roadmap.md
new file mode 100644
index 0000000..6669607
--- /dev/null
+++ b/doc/camera_number_roadmap.md
@@ -0,0 +1,275 @@
+# Roadmap: Wie viele Kameras braucht die Pose-Rekonstruktion wirklich?
+
+## Ziel
+
+Untersuchen, ob die vollständige Roboter-Pose mit **3 Kameras** ausreichend präzise rekonstruiert werden kann, verglichen mit der aktuellen Konfiguration mit **6–8 Kameras**.
+
+Die Kernfrage ist:
+
+**X-Achse:** Anzahl verwendeter Kameras
+**Y-Achse:** Genauigkeit der rekonstruierten Pose, gemessen als **Fingerspitzen-Abweichung in mm**
+
+---
+
+## 1. Was dafür bekannt sein muss
+
+Bevor die Auswertung sinnvoll ist, sollten diese Punkte klar dokumentiert sein:
+
+### 1.1 Datenbasis
+
+* Wie viele Szenen existieren insgesamt?
+* Welche Posen decken die Szenen ab?
+* Sind die Szenen gleichmäßig über den Arbeitsraum verteilt oder nur ein Teilbereich?
+* Gibt es Wiederholungen derselben Pose unter leicht anderen Bedingungen?
+
+### 1.2 Kamera-Setup
+
+* Welche Kameras sind in jeder Szene aktiv?
+* Sind alle Kameras synchronisiert?
+* Sind alle Kameras kalibriert?
+* Sind intrinsische und extrinsische Parameter pro Kamera vorhanden?
+* Gibt es Ausfälle, verdeckte Sicht oder variable Bildqualität?
+
+### 1.3 Ground Truth
+
+* Wie werden die „echten“ Roboter-Posen gespeichert?
+* Ist die Ground Truth in einem konsistenten Koordinatensystem verfügbar?
+* Ist die Fingerspitzenposition direkt ableitbar oder muss sie aus der Pose berechnet werden?
+* Welche Referenz gilt als Wahrheit: Robotermodell, Encoder-Daten, Simulation oder beides?
+
+### 1.4 Rekonstruktionspipeline
+
+* Welche Zwischenresultate liefert die Pipeline?
+
+  * Detektion im Bild
+  * Modellanpassung
+  * 2D/3D-Keypoints
+  * vollständige Pose
+* Ist die Pipeline deterministisch oder stochastisch?
+* Welche Fehlerquellen sind bereits bekannt?
+
+### 1.5 Bewertungsmetrik
+
+* Wird nur die Fingerspitze bewertet oder auch weitere Gelenkpunkte?
+* Ist die Abweichung als **mittlere Distanz**, **Median**, **RMSE** oder **95%-Quantil** interessant?
+* Soll die Auswertung pro Szene, pro Kamera-Subset und aggregiert über alle Szenen erfolgen?
+
+---
+
+## 2. Was umgesetzt werden muss
+
+### 2.1 Dateninventur und Datenformat
+
+Zuerst sollte eine saubere Übersicht aller Szenen entstehen:
+
+* Szenen-ID
+* verfügbare Kameras
+* Zeitstempel / Synchronisationsstatus
+* Ground-Truth-Pose
+* rekonstruierte Pose
+* Bildqualität oder Sichtbarkeitsstatus
+
+Empfehlung: ein tabellarisches Metadatenformat, z. B. CSV, JSON oder eine kleine Datenbank.
+
+### 2.2 Subset-Definition für Kameras
+
+Für den Vergleich muss festgelegt werden, **welche 3 Kameras** verwendet werden.
+
+Mögliche Varianten:
+
+* feste Auswahl der besten 3 Kameras
+* alle Kombinationen aus 3 Kameras
+* Auswahl nach Sichtbarkeit / Geometrie / Robustheit
+
+Wichtig: Die Wahl der 3 Kameras beeinflusst das Ergebnis stark. Deshalb sollte nicht nur eine Kombination getestet werden, sondern möglichst mehrere.
+
+### 2.3 Rekonstruktion pro Kameraset
+
+Die Pipeline muss für verschiedene Kamerakombinationen erneut laufen:
+
+* 3 Kameras
+* 4 Kameras
+* 5 Kameras
+* 6 Kameras
+* 7 Kameras
+* 8 Kameras
+
+Optional zusätzlich:
+
+* jede einzelne Kamera weglassen, um die Sensitivität zu messen
+* nur die geometrisch günstigsten Kameras verwenden
+
+### 2.4 Fehlerberechnung
+
+Für jede Szene und jedes Kameraset:
+
+1. Rekonstruierte Pose erzeugen
+2. Fingerspitze aus rekonstruierter Pose bestimmen
+3. Ground-Truth-Fingerspitze bestimmen
+4. Abstand in Millimetern berechnen
+5. Ergebnis speichern
+
+Empfohlenes Ergebnisformat pro Versuch:
+
+* Szene
+* Kameraset-ID
+* Anzahl Kameras
+* Fingerpunkt-Fehler [mm]
+* weitere optionale Metriken, z. B. Gelenkfehler, Sichtbarkeitsrate, Rekonstruktionsqualität
+
+### 2.5 Aggregation und Auswertung
+
+Am Ende sollten Kennzahlen über alle Szenen berechnet werden:
+
+* Mittelwert des Fehlers
+* Median des Fehlers
+* Standardabweichung
+* 95%-Konfidenzintervall oder Bootstrap-Intervall
+* Fehlerverteilung pro Kamerazahl
+
+Zusätzlich hilfreich:
+
+* Boxplots je Kamerazahl
+* Fehlerbalken mit Konfidenzintervallen
+* Plot der besten / schlechtesten Kamerakombinationen
+
+---
+
+## 3. Empfohlene Auswertungslogik
+
+### 3.1 Vergleich nach Kamerazahl
+
+Für jede Kamerazahl k:
+
+* alle relevanten Kamerakombinationen testen oder eine definierte Auswahl bilden
+* Fehler pro Szene berechnen
+* Ergebnisse aggregieren
+
+So entsteht die Kurve:
+
+* 3 Kameras → mittlere Abweichung
+* 4 Kameras → mittlere Abweichung
+* ...
+* 8 Kameras → mittlere Abweichung
+
+### 3.2 Vergleich nach Kamerakombination
+
+Nicht nur die Anzahl zählt, sondern auch die Anordnung.
+
+Deshalb sollte zusätzlich ausgewertet werden:
+
+* welche 3-Kamera-Kombination am besten ist
+* ob bestimmte Kameras besonders wichtig sind
+* ob eine gute Geometrie wichtiger ist als reine Anzahl
+
+### 3.3 Robustheit über Szenen
+
+Die Frage ist nicht nur „Was ist im Mittel gut?“, sondern auch:
+
+* Gibt es Szenen, in denen 3 Kameras deutlich scheitern?
+* Gibt es Posen, bei denen schon 3 Kameras reichen?
+* Ist der Fehler bei bestimmten Roboterausrichtungen systematisch höher?
+
+---
+
+## 4. Praktische Umsetzungsschritte
+
+### Phase A: Datengrundlage sichern
+
+* Alle Szenen inventarisieren
+* Kamerazustand pro Szene prüfen
+* Ground Truth und Rekonstruktionen in ein konsistentes Format bringen
+* Ein eindeutiges Schema für Szenen- und Kameraset-IDs definieren
+
+### Phase B: Vergleichsdesign festlegen
+
+* Festlegen, ob alle 3er-Kombinationen getestet werden oder nur ausgewählte Sets
+* Definieren, welche Kameraauswahl als Referenz dient
+* Fehlermaß final festlegen
+
+### Phase C: Batch-Auswertung bauen
+
+* Pipeline über mehrere Kamerasets automatisieren
+* Ergebnisse versionieren und speichern
+* Laufzeit und Fehler robust protokollieren
+
+### Phase D: Statistische Analyse
+
+* Pro Kamerazahl Mittelwert, Median und Streuung berechnen
+* Signifikanztests oder Bootstrap-Vergleiche zwischen Kamerazahlen durchführen
+* Ausreißer identifizieren
+
+### Phase E: Visualisierung und Entscheidung
+
+* Plot „Anzahl Kameras vs. Fehler in mm“
+* Plot pro Szene oder pro Posegruppe
+* Entscheidungsregel ableiten, ab wann zusätzliche Kameras kaum noch Verbesserungen bringen
+
+---
+
+## 5. Wichtige Fragen, die vorab beantwortet sein sollten
+
+1. Welche 3 Kameras sind gemeint: beliebige, beste, feste oder geometrisch ausgewählte?
+2. Sind alle Kameras pro Szene vorhanden oder gibt es Lücken?
+3. Wie genau wird die Fingerspitze aus der Robotermodell-Pose berechnet?
+4. Soll der Fehler nur an einem Punkt oder über mehrere Posepunkte bewertet werden?
+5. Wie wird mit Szenen umgegangen, in denen eine Rekonstruktion scheitert?
+6. Ist die Messung in Simulationsdaten, Realwelt oder gemischt?
+7. Soll die Auswertung pro Szene oder über alle Szenen gepoolt erfolgen?
+
+---
+
+## 6. Empfohlenes Ergebnis der Analyse
+
+Am Ende sollte die Auswertung mindestens diese Ergebnisse liefern:
+
+* eine Tabelle mit Fehlern pro Szene und Kameraset
+* ein Diagramm „Kameraszahl vs. mittlere Fingerspitzen-Abweichung“
+* eine Aussage, ob 3 Kameras praktisch ausreichend sind
+* eine Aussage, welche Kameras oder Geometrien besonders wichtig sind
+* eine Empfehlung für ein Minimal-Setup mit akzeptabler Genauigkeit
+
+---
+
+## 7. Mögliche Entscheidungslogik
+
+Eine einfache Entscheidungsregel könnte sein:
+
+* 3 Kameras sind ausreichend, wenn der mittlere Fehler nur wenig schlechter ist als bei 6–8 Kameras
+* die Verteilung der Fehler bei 3 Kameras darf nicht zu viele Ausreißer enthalten
+* das System muss für die meisten Szenen stabil bleiben
+
+Beispiel für eine praktische Schwelle:
+
+* maximal zulässige mittlere Abweichung
+* maximal zulässiger Fehler in 95% der Fälle
+* maximaler Verlust gegenüber der Vollkonfiguration
+
+Diese Schwelle sollte fachlich mit der Anwendung abgestimmt werden.
+
+---
+
+## 8. Nächste konkrete Arbeitspakete
+
+1. Metadaten aller Szenen konsolidieren
+2. Ground Truth und Rekonstruktionsausgabe vereinheitlichen
+3. Kameraset-Strategie festlegen
+4. Batch-Runner für mehrere Kamerakonfigurationen bauen
+5. Fingerpunkt-Fehler je Szene berechnen
+6. Aggregation und Plots erstellen
+7. Ergebnis interpretieren und Empfehlung ableiten
+
+---
+
+## 9. Kurzfassung
+
+Die Kernaufgabe ist nicht nur ein einfacher Plot, sondern ein **systematischer Vergleich verschiedener Kamerasets**. Dafür braucht es:
+
+* saubere Ground Truth
+* konsistente Kamerakalibrierung
+* definierte Kamerakombinationen
+* automatisierte Rekonstruktion
+* robuste Fehlerberechnung in mm
+* aggregierte Analyse über alle Szenen
+
+Erst dann lässt sich belastbar sagen, ob **3 Kameras genügen** oder ob die zusätzlichen Kameras einen messbaren Mehrwert bringen.
diff --git a/doc/docker_containerization_roadmap.md b/doc/docker_containerization_roadmap.md
new file mode 100644
index 0000000..04c2847
--- /dev/null
+++ b/doc/docker_containerization_roadmap.md
@@ -0,0 +1,420 @@
+# Docker Containerization Roadmap
+
+## Ziel
+
+Die Blender-Rendering-Pipeline soll vollständig containerisiert und später in eine verteilte Job-Infrastruktur integriert werden.
+
+Wichtige Randbedingungen:
+
+* Blender-Container sollen nur laufen, wenn tatsächlich Renderaufträge vorhanden sind.
+* Verschiedene Jobtypen sollen unterschiedliche Container verwenden können.
+* Die Infrastruktur soll zukünftig nicht nur Blender-Rendering, sondern auch andere Workloads (AI-Training, STL-Verarbeitung, Datensatz-Generierung, Batch-Konvertierungen usw.) unterstützen.
+* Die Ausführung soll auf mehreren PCs/Worker-Nodes möglich sein.
+* Die bestehende Python-Logik soll möglichst unverändert bleiben.
+
+---
+
+# Review / Bewertung
+
+**Gesamteinschätzung:** schlüssig und machbar. Die Zielarchitektur (Redis-Queue +
+Nomad-Scheduler, Container nur bei Bedarf, mehrere Workload-Typen, horizontale Skalierung)
+ist tragfähig und zukunftssicher. Es fehlen einige technische Punkte, die unten ergänzt sind.
+
+## Wichtigste Korrektur: Renderer und Pipeline sind ZWEI Container, nicht einer
+
+Phase 1 spricht von „Rendering-Pipeline **+** Analyse-Pipeline" in *einem* Container.
+Das sollte getrennt werden — die beiden haben grundverschiedene Profile:
+
+| | Render-Container | Pipeline-Container |
+|---|---|---|
+| Image | `approbot/blender-renderer` | `approbot/pose-pipeline` |
+| Basis | Blender 4.5 + bpy + cv2 + numpy (~1–2 GB) | `python:3.11-slim` + numpy/scipy/opencv (~200 MB) |
+| GPU | ja (Cycles, CUDA/OPTIX) | nein |
+| Zweck | **Test-Datengenerator** (nur Sim/Dev) | **Produkt** (läuft auch mit echten Webcam-Fotos!) |
+| Änderungsrate | selten | häufig |
+| CI-tauglich | nein (schwer, GPU) | ja (schnell, headless) |
+
+Das ist keine Kosmetik: Die **Pipeline ist das eigentliche Deliverable** (Roboter-Pose
+aus Fotos) und wird real deployed; der Renderer ist nur Werkzeug zur Testdaten-Erzeugung.
+Getrennt heißt: die Pipeline bleibt schlank, schnell CI-testbar und ohne Blender-Ballast
+deploybar; der Renderer wird nur dort gestartet, wo eine GPU steht.
+
+**Datenaustausch** zwischen beiden: gemeinsames Volume (Phase 1–3), später Object-Storage
+(MinIO/S3) im verteilten Betrieb.
+
+## Technische Schlüsselpunkte (bisher nicht in der Roadmap)
+
+1. **cv2 in Blenders Python:** `render_robot.py` importiert `cv2` (ArUco-Generierung).
+   Blender bringt sein *eigenes* gebündeltes Python mit — `opencv-python` muss **dort**
+   installiert werden (`<blender>/python/bin/python -m pip install opencv-python`),
+   nicht ins System-Python. Häufige Stolperfalle.
+2. **ArUco = opencv-contrib:** Die Pipeline nutzt `cv2.aruco`. Im Pipeline-Container
+   `opencv-contrib-python-headless` verwenden (nicht Basis-`opencv-python`), Version pinnen.
+3. **robot.json-Mutation ist im Parallelbetrieb ein Race:** `render_Loop.py` schreibt
+   pro Render in die *gemeinsame* `robot.json` (Kamera, Pose, Auflösung). Laufen mehrere
+   Render-Jobs parallel auf einem Node, überschreiben sie sich gegenseitig. Im Job-Modell
+   muss jeder Job seine Parameter aus dem **Job-Payload** bekommen und in eine **eigene
+   temporäre Config** schreiben — nicht in die geteilte robot.json. (Architektur-Fix,
+   gehört *vor* Phase 4.)
+4. **GPU im Container & Scheduler:** `--gpus all` + NVIDIA Container Toolkit; Nomad braucht
+   das GPU-Device-Plugin, damit Render-Jobs nur auf GPU-Nodes landen. CPU-Fallback (Cycles
+   `device='CPU'`) als Sicherheitsnetz behalten.
+5. **Versionen pinnen:** Blender 4.5.x exakt, `requirements.txt` mit festen Versionen
+   (numpy/scipy/opencv) — sonst driften die Mess-Ergebnisse (vgl. der fy-Intrinsik-Bug).
+6. **Job-Granularität:** `{"jobType":"blender-render","pose":8}` rendert 7 Kameras pro Job.
+   Feinkörniger (1 Job = 1 Kamera) parallelisiert besser über mehrere Nodes — abwägen.
+
+## Empfehlung: MVP vor Nomad
+
+Redis + Nomad ist die richtige End-Vision, aber als Zwischenstufe genügt **docker-compose
+mit zwei Images + Profiles** (Renderer on-demand via `docker compose run`, Pipeline als
+schneller Service / CI-Step). Damit sind getrennte Container + Phase 1–3 produktiv nutzbar,
+bevor die verteilte Orchestrierung (Phase 5–8) aufgebaut wird.
+
+---
+
+# Phase 1 – Dockerisierung der bestehenden Rendering-Pipeline
+
+## Ziel
+
+Die aktuelle Blender-Pipeline läuft vollständig innerhalb eines Docker-Containers.
+
+### Aufgaben
+
+* [x] Dockerfile erstellen → **in zwei aufteilen:** `Dockerfile.renderer` (Blender) + `Dockerfile.pipeline` (slim Python)
+* [x] docker-compose.yaml erstellen
+* [ ] cv2 in Blenders gebündeltem Python installieren (für ArUco-Generierung)
+* [ ] requirements.txt für den Pipeline-Container (numpy, scipy, opencv-contrib-python-headless), Versionen gepinnt
+* [ ] Blender 4.5 Container erfolgreich starten
+* [ ] render_loop.py innerhalb des Containers ausführen
+* [ ] render_robot.py innerhalb des Containers ausführen
+* [ ] STL-Import validieren
+* [ ] PNG-Ausgabe validieren
+* [ ] NPZ-Ausgabe validieren
+* [ ] Pipeline auf PNG-Ausgaben ermöglichen
+* [ ] markers.json validieren
+
+### Ergebnis
+
+Zwei Container: `blender-renderer` erzeugt Bilder/npz/Ground-Truth, `pose-pipeline`
+wertet sie aus — beide reproduzierbar, über ein gemeinsames Daten-Volume verbunden.
+
+---
+
+# Phase 2 – Pfad-Unabhängigkeit
+
+## Ziel
+
+Alle absoluten Benutzerpfade entfernen.
+
+### Aktuelle Situation
+
+Beispiel:
+
+```python
+Path.home() / "SynologyDrive" / ...
+```
+
+### Ziel
+
+Container-interne Pfade verwenden:
+
+```text
+/workspace/data
+/workspace/setup
+/workspace/output
+```
+
+### Aufgaben
+
+* [ ] ROBOT_JSON_FILE auf Containerpfade umstellen
+* [ ] OUTPUT_FILE auf Containerpfade umstellen
+* [ ] STL-Suche auf Containerpfade umstellen
+* [ ] Konfiguration über Environment-Variablen ermöglichen
+* [ ] **render_robot.py: Config-Pfad per env (`ROBOT_JSON`)** statt hardcodiert; und
+  **render_Loop.py: pro Render eine *temporäre* Config schreiben** statt die geteilte
+  robot.json zu mutieren (Voraussetzung für parallele Jobs, siehe Schlüsselpunkt 3)
+* [ ] BLENDER_EXE per env/PATH (aktuell hardcodierter Windows-Pfad)
+
+### Ergebnis
+
+Die Pipeline ist vollständig unabhängig vom lokalen Benutzerprofil und parallel-tauglich.
+
+---
+
+# Phase 3 – Repository-Struktur bereinigen
+
+## Ziel
+
+Klare Trennung zwischen Code, Daten und Ergebnissen.
+
+### Zielstruktur
+
+```text
+appRobotRendering/
+
+├── data/
+│   ├── robot/
+│   ├── simulation/
+│   └── surfaces/
+│
+├── setup/
+│   └── generateSets/
+│       ├── render_loop.py
+│       ├── render_robot.py
+│       ├── Dockerfile
+│       └── docker-compose.yaml
+│
+├── jobs/
+│
+└── output/
+```
+
+### Aufgaben
+
+* [ ] Verzeichnisstruktur vereinheitlichen
+* [ ] Dokumentation ergänzen
+* [ ] Mountpoints definieren
+
+---
+
+# Phase 4 – Job-Modell einführen
+
+## Ziel
+
+Rendering-Aufträge werden als eigenständige Jobs beschrieben.
+
+### Beispiel
+
+```json
+{
+  "jobType": "blender-render",
+  "pose": 8
+}
+```
+
+### Spätere Erweiterungen
+
+```json
+{
+  "jobType": "dataset-generation"
+}
+```
+
+```json
+{
+  "jobType": "stl-conversion"
+}
+```
+
+```json
+{
+  "jobType": "ai-training"
+}
+```
+
+### Aufgaben
+
+* [ ] Job-Format definieren
+* [ ] Job-Metadaten definieren
+* [ ] Retry-Konzept definieren
+* [ ] Statusmodell definieren
+
+### Status
+
+```text
+queued
+running
+completed
+failed
+timeout
+```
+
+---
+
+# Phase 5 – Redis als Queue
+
+## Ziel
+
+Entkopplung zwischen Auftragserzeugung und Ausführung.
+
+### Aufgaben
+
+* [ ] Redis bereitstellen
+* [ ] Job-Queue definieren
+* [ ] Job-Status speichern
+* [ ] Retry-Mechanismus definieren
+
+### Hinweis
+
+Redis startet keine Worker.
+
+Redis dient ausschließlich als:
+
+* Queue
+* Statusspeicher
+* Kommunikationsschicht
+
+---
+
+# Phase 6 – Nomad Scheduler
+
+## Ziel
+
+Container werden nur bei tatsächlichem Bedarf gestartet.
+
+### Architektur
+
+```text
+Job Producer
+      │
+      ▼
+
+    Redis
+      │
+      ▼
+
+    Nomad
+      │
+      ▼
+
+ Docker Container
+```
+
+### Aufgaben
+
+* [ ] Nomad Server aufsetzen
+* [ ] Worker-Nodes registrieren
+* [ ] Docker Driver aktivieren
+* [ ] Batch-Jobs definieren
+
+### Ergebnis
+
+Nomad entscheidet:
+
+* welcher Node frei ist
+* welcher Container gestartet wird
+* wann ein Container beendet wird
+
+---
+
+# Phase 7 – Blender als Nomad Batch Job
+
+## Ziel
+
+Blender läuft nur während der Bearbeitung eines Auftrags.
+
+### Beispiel
+
+```text
+Job:
+blender-render
+
+Container:
+approbot/blender-renderer:latest
+```
+
+Ablauf:
+
+1. Job wird eingereicht
+2. Nomad startet Blender-Container
+3. Rendering läuft
+4. Ergebnisse werden gespeichert
+5. Container beendet sich
+6. Ressourcen werden freigegeben
+
+### Ergebnis
+
+Keine dauerhaft laufenden Blender-Worker.
+
+---
+
+# Phase 8 – Multi-Container Plattform
+
+## Ziel
+
+Beliebige Jobtypen auf derselben Infrastruktur ausführen.
+
+### Beispiele
+
+#### Blender Rendering
+
+```text
+Container:
+approbot/blender-renderer
+```
+
+#### AI Training
+
+```text
+Container:
+approbot/trainer
+```
+
+#### STL Processing
+
+```text
+Container:
+approbot/stl-worker
+```
+
+#### Dataset Generation
+
+```text
+Container:
+approbot/dataset-worker
+```
+
+### Ergebnis
+
+Der verwendete Container wird durch den Jobtyp bestimmt.
+
+---
+
+# Zielarchitektur
+
+```text
+                    Nomad
+                       │
+
+      ┌────────────────┼────────────────┐
+      │                │                │
+
+ Blender Job      AI Job         Dataset Job
+      │                │                │
+
+ Blender       Training      Dataset Worker
+ Container      Container      Container
+
+      └────────────────┼────────────────┘
+                       │
+
+                    Docker
+
+                       │
+
+              Worker Nodes (PCs)
+```
+
+Eigenschaften:
+
+* keine dauerhaft laufenden Blender-Container
+* unterschiedliche Container pro Jobtyp
+* horizontale Skalierung auf mehrere PCs
+* saubere Trennung von Scheduling und Ausführung
+* zukünftige Erweiterbarkeit für beliebige Workloads
+
+---
+
+# Offene Punkte / nicht vergessen
+
+* **Cluster-Storage:** geteiltes NFS/Volume (einfach) vs. Object-Storage MinIO/S3
+  (entkoppelt Nodes, skaliert). Ab >1 Worker-Node bevorzugt Object-Storage.
+* **robot.json als geteilter Mutable-State auflösen** (Schlüsselpunkt 3) — Voraussetzung
+  für parallele Render-Jobs auf einem Node.
+* **Reproduzierbarkeit:** Blender- und Python-Versionen pinnen; `requirements.txt` einchecken.
+* **GPU-Headless:** prüfen, ob Cycles im Container EGL braucht; sonst CPU-Fallback verifizieren.
+* **CI-Hook:** Pipeline-Container in CI → `benchmark/run_benchmark.py` gegen Ground-Truth
+  bei jedem Commit (schnell, ohne Blender) — fängt Regressionen wie den fy-Bug automatisch.
+* **Monitoring/Logs:** Job-Logs zentral (stdout → Nomad/Loki), Render-Zeiten messen.
+* **Image-Registry:** private Registry (Harbor o. Ä.) für die `approbot/*`-Images.
diff --git a/doc/improvements_roadmap.md b/doc/improvements_roadmap.md
new file mode 100644
index 0000000..dfbe3a0
--- /dev/null
+++ b/doc/improvements_roadmap.md
@@ -0,0 +1,30 @@
+# Nötige Anpassungen #
+
+## Marker ##
+
+In der Render-Pipeline werden die Marker zufällit "falsch aufgeklebt" 
+das passiert momentan in alle Raumrichtungen falsch (soweit ich weiss).
+Realistisch ist nur ein Verschieben senkrecht zur Normalen. Der Aufkleber
+der falsch angebracht wird. Die Normale, der Untergrund verändert sich am 
+Arm nicht.
+
+Anders sieht es auf der Base aus. Hier skann es passieren, dass die "unteren"
+Marker sich auf dem Papier verwerfen. Die Marker sind bei -20mm angebracht,
+auf einem Papier. und das kann sich falten oder verwerfen. Hier muss noch überlegt 
+werden, wie das gut simuliert werden kann.
+
+## Gewichtung ## 
+
+Die Marker-Gewichtung wurde glaub ich noch nicht angemessen umgesetzt, oder?
+
+
+
+## Versionierung ##
+
+Es muss klar sein, mit welcher Version der Ausgabe, mit welcher Version des Robot.json gearbeitet
+wurde. Dazu braucht es
+
+1) Label in allen Python Programmen, dass klar ist, welches Python Programm hier grad aktiv ist
+2) Label im robot.json
+3) Jeweils die Nachvollziehbarkeit in den erzeugten Auswertungs.json
+
diff --git a/doc/lens_errors_roadmap.md b/doc/lens_errors_roadmap.md
new file mode 100644
index 0000000..1a3420d
--- /dev/null
+++ b/doc/lens_errors_roadmap.md
@@ -0,0 +1,103 @@
+# Roadmap: Kamera-/Linsenfehler simulieren (billige WebCams)
+
+**Status:** ✅ umgesetzt (A–D) in `setup/generateSets/render_robot.py`, Parameter in
+`robot.json → renderingInfo`. **A ist isoliert getestet** (deterministisches npz-Rauschen).
+**B–D sind Compositor-Effekte und müssen einmal in Blender 4.5 visuell geprüft werden** —
+jeder Effekt ist in `try/except` gekapselt: ein API-Problem überspringt nur den betroffenen
+Effekt mit Log-Warnung `[render][WARN] <effekt> skipped: ...`, der Render läuft weiter.
+
+### Parameter (robot.json → renderingInfo)
+
+| Effekt | Parameter | Beispiel |
+|---|---|---|
+| A Brennweiten-Restfehler | `focalErrorPct` | 0.5 (±%) |
+| A Hauptpunkt-Versatz | `principalErrorPx` | 3.0 (±px) |
+| A Rest-Verzeichnung | `residualDistortion` | [0.02, -0.01] (k1,k2) |
+| B Linsenstaub | `lensDirt` / `lensDirtStrength` | true / 0.08 |
+| C Vignette | `vignette` / `vignetteStrength` | true / 0.08 |
+| C Rand-Unschärfe | `localizedBlur` / `localizedBlurStrength` | false / 0.15 |
+| D Sensor-Rauschen | `sensorNoise` / `sensorNoiseStrength` | true / 0.01 |
+| D chromat. Aberration | `lensDistortion` / `lensDistortionStrength` | true / 0.002 |
+
+**Wichtig:** A verfälscht nur die **npz-Kalibrierung** (Bild bleibt ideal) → echter
+geometrischer Härtetest gegen unvollständige Kalibrierung; deterministisch pro Kamera
+(Seed aus `cameraPosition`), damit über alle Posen konsistent. B–D ändern nur das **Bild**
+(Detektion erschwert), nicht die Ground-Truth.
+
+---
+
+(Ursprüngliche Detailplanung als Referenz:)
+
+**Ziel:** Realistische WebCam-Fehler in den Blender-Renderer (`setup/generateSets/render_robot.py`)
+einbauen, um die Pose-Pipeline gegen reale Kamera-Imperfektionen zu härten — analog
+zu den bereits umgesetzten Marker-Störungen (`markerOffsetMaxMm`, `motionBlur`, `arucoDust`).
+
+## Kernkonzept (wichtig!)
+
+Es gibt zwei Fehlerklassen:
+
+1. **Geometrische Fehler** (Brennweite, Hauptpunkt, Verzeichnung): verschieben die
+   Marker-Pixel. **Entscheidend:** Kennt die Kalibrierung (`render_*.npz`) den Fehler
+   exakt, korrigiert die `undistort`-Kette ihn vollständig → *kein* Pose-Fehler. Den
+   Fehler erzeugt nur die **unvollständige Kalibrierung**. Daher: nicht „Bild verzeichnen",
+   sondern die **npz-Intrinsik leicht von der Wahrheit abweichen lassen** (Kalibrier-Restfehler).
+2. **Photometrische Fehler** (Staub, Vignette, Rand-Unschärfe, Rauschen): ändern die npz
+   nicht, erschweren nur die Detektion (verrauschte Ecken, fehlende Marker).
+
+## Hinweis: tote Parameter
+
+In `robot.json → renderingInfo` existieren bereits, werden aber von `render_robot.py`
+**nicht angewandt**: `lensDirt`, `lensDirtStrength`, `vignette`, `vignetteStrength`,
+`sensorNoise`, `sensorNoiseStrength`, `lensDistortion`, `lensDistortionStrength`,
+`localizedBlur`, `localizedBlurStrength`. Diese zum Leben erwecken.
+
+## A — Kalibrier-Restfehler (geometrisch) ⭐ zuerst
+
+Höchster Testwert, deckt eine Lücke der bisherigen Störungen ab. Modell-konsistent
+(kein Blender↔OpenCV-Mismatch), weil das Bild ideal bleibt und nur die *angenommene*
+Kalibrierung in der npz falsch ist.
+
+**Implementierung:** im npz-Schreibblock von `render_robot.py` (~Zeile 460–510, wo
+`camera_matrix`/`dist_coeffs` gebaut werden) die Werte gezielt verrauschen —
+**deterministisch pro Kamera** (Seed aus Kameraposition/-id), damit über alle Posen konsistent.
+
+Neue robot.json-Parameter (Vorschlag):
+- `focalErrorPct` (z. B. 0.5) → `fx,fy *= 1 ± rnd·focalErrorPct/100`
+- `principalErrorPx` (z. B. 3) → `cx,cy += rnd·principalErrorPx`
+- `residualDistortion` (z. B. [0.02, -0.01]) → kleine `k1,k2` in `dist_coeffs`,
+  die die Pipeline „korrigiert", obwohl das Bild ideal ist (Rest-Verzeichnung)
+
+## B — Staub auf der Linse (photometrisch)
+
+Halbtransparentes Schmutz-Overlay über dem *ganzen* Bild (Compositor), verdeckt zufällig
+Marker teilweise. Parameter: `lensDirt`, `lensDirtStrength`.
+
+**Implementierung:** Compositor — RenderLayers → Mix (über eine prozedurale Fleckentextur,
+Voronoi/Noise mit hoher Schwelle) → Composite. Wenige, weiche, leicht abdunkelnde Flecken.
+
+## C — Vignette + Rand-Unschärfe (Feldkrümmung)
+
+Randabdunklung und radial nach außen zunehmende Unschärfe (billige Linsen sind am Rand
+schlechter). Parameter: `vignette`/`vignetteStrength`, `localizedBlur`/`localizedBlurStrength`.
+
+**Implementierung:** Compositor — Vignette via Ellipse-Maske × Bild; Rand-Unschärfe via
+`CompositorNodeBlur` gemischt über eine radiale Maske (Lens Distortion „Distort" auf einer
+Maske oder Ellipse-Maske als Mix-Faktor zwischen scharf und Blur).
+
+## D — Sensor-Rauschen + chromatische Aberration
+
+Leichtes Pixelrauschen und Farbsäume an Kanten. Parameter: `sensorNoise`/`sensorNoiseStrength`,
+`lensDistortion`/`lensDistortionStrength` (Dispersion).
+
+**Implementierung:** Compositor — additives Noise (klein) auf das Bild; chromatische
+Aberration via `CompositorNodeLensdist` mit kleinem `dispersion`-Wert.
+
+## Reihenfolge & Test
+
+1. **A** (geometrisch, einfach, konsistent) — danach Benchmark *mit* vs. *ohne* laufen lassen.
+2. **B + C** (Detektions-Robustheit).
+3. **D** (Feinschliff).
+
+Wichtig: Compositor-Node-API variiert leicht je Blender-Version — Code im echten Blender
+(4.5) gegenchecken. Determinismus pro Kamera nur bei A nötig (geometrische Konsistenz);
+B–D dürfen pro Aufnahme zufällig sein.
diff --git a/setup/generateSets/Dockerfile b/setup/generateSets/Dockerfile
new file mode 100644
index 0000000..ee057e6
--- /dev/null
+++ b/setup/generateSets/Dockerfile
@@ -0,0 +1,9 @@
+FROM linuxserver/blender:latest
+
+USER root
+
+RUN pip3 install --no-cache-dir \
+    numpy \
+    opencv-python
+
+WORKDIR /workspace
\ No newline at end of file
diff --git a/setup/generateSets/__pycache__/render_robot.cpython-311.pyc b/setup/generateSets/__pycache__/render_robot.cpython-311.pyc
index c6f39ed..4361159 100644
Binary files a/setup/generateSets/__pycache__/render_robot.cpython-311.pyc and b/setup/generateSets/__pycache__/render_robot.cpython-311.pyc differ
diff --git a/setup/generateSets/docker-compose.yaml b/setup/generateSets/docker-compose.yaml
new file mode 100644
index 0000000..e69de29
diff --git a/setup/generateSets/render_Loop.py b/setup/generateSets/render_Loop.py
index 76cc0fa..01cd5d6 100644
--- a/setup/generateSets/render_Loop.py
+++ b/setup/generateSets/render_Loop.py
@@ -96,11 +96,13 @@ def split_pose(pose_entry):
 
 def main():
     USER_HOME = Path.home()
-    BASE = USER_HOME / "SynologyDrive" / "2026-AppServer-AppRobot" / "appRobotRendering"
+    BASE = Path(__file__).resolve().parents[2]
+
+
+    BLENDER_EXE = str("C:/Program Files/Blender Foundation/Blender 4.5/blender.exe")
 
     ROBOT_JSON_FILE = str(BASE / "data" / "robot" / "robot.json")
     OUTPUT_DIR = str(BASE / "data" / "simulation" / "debug")
-    BLENDER_EXE = str("C:/Program Files/Blender Foundation/Blender 4.5/blender.exe")
     RENDER_PY = str(BASE / "setup" / "generateSets" / "render_robot.py")
     RENDER_PNG = str(BASE / "data" / "simulation" / "debug" / "render.png")
     OUTPUT_SET = str(BASE / "data" / "simulation")
diff --git a/setup/generateSets/render_robot.py b/setup/generateSets/render_robot.py
index e139a12..f3bf939 100644
--- a/setup/generateSets/render_robot.py
+++ b/setup/generateSets/render_robot.py
@@ -18,9 +18,11 @@ from mathutils import Matrix
 # Holt dynamisch den Pfad zum aktuellen Benutzerverzeichnis (z.B. C:\Users\Name)
 USER_HOME = Path.home()
 
+BASE = Path(__file__).resolve().parents[2]
+
 # Kombiniert den Benutzerpfad mit dem spezifischen Ordnerpfad und konvertiert direkt zu str
-ROBOT_JSON_FILE = str(USER_HOME / "SynologyDrive" / "2026-AppServer-AppRobot" / "appRobotRendering" / "data" / "robot" / "robot.json")
-OUTPUT_FILE = str(USER_HOME / "SynologyDrive" / "2026-AppServer-AppRobot" / "appRobotRendering" / "data" / "simulation" / "debug" / "render.png")
+ROBOT_JSON_FILE = str(BASE / "data" / "robot" / "robot.json")
+OUTPUT_FILE = str(BASE / "data" / "simulation" / "debug" / "render.png")
 
 print("Using robot JSON file:", ROBOT_JSON_FILE)
 print("Using output file:", OUTPUT_FILE)
@@ -88,6 +90,23 @@ marker_rot_max_deg   = float(rendering_info.get("markerRotationMaxDeg", 0.0))
 motion_blur          = as_bool(rendering_info.get("motionBlur", False))
 motion_blur_max_px   = float(rendering_info.get("motionBlurMaxPx", 1.5))
 
+# ── Linsen-/Kamerafehler ───────────────────────────────────────────
+# (A) Kalibrier-Restfehler: die npz-Intrinsik leicht von der Wahrheit abweichen lassen
+#     (deterministisch pro Kamera -> über alle Posen konsistent). Das Bild bleibt ideal,
+#     nur die *angenommene* Kalibrierung ist falsch — wie bei realer Webcam-Kalibrierung.
+intr_focal_err_pct = float(rendering_info.get("focalErrorPct", 0.0))      # ±% auf fx, fy
+intr_principal_px  = float(rendering_info.get("principalErrorPx", 0.0))   # ±px auf cx, cy
+intr_residual_dist = rendering_info.get("residualDistortion", None)        # [k1, k2] in dist_coeffs
+# (B-D) photometrische Effekte (Compositor)
+vignette                 = as_bool(rendering_info.get("vignette", False))            # C
+vignette_strength        = float(rendering_info.get("vignetteStrength", 0.25))
+localized_blur           = as_bool(rendering_info.get("localizedBlur", False))       # C
+localized_blur_strength  = float(rendering_info.get("localizedBlurStrength", 0.15))
+sensor_noise             = as_bool(rendering_info.get("sensorNoise", False))         # D
+sensor_noise_strength    = float(rendering_info.get("sensorNoiseStrength", 0.02))
+lens_distortion          = as_bool(rendering_info.get("lensDistortion", False))      # D (chromat. Aberration)
+lens_distortion_strength = float(rendering_info.get("lensDistortionStrength", 0.01))
+
 state: Dict[str, float] = {k: 0.0 for k in STATE_KEYS}
 for source_name in ("defaultPosition", "recognized", "movements"):
     source = robot.get(source_name, {}) or {}
@@ -518,6 +537,28 @@ camera_matrix = np.array([
 # ideal synthetic camera
 dist_coeffs = np.zeros((5, 1), dtype=np.float32)
 
+# ── (A) Kalibrier-Restfehler: Intrinsik gezielt verfälschen ──
+# Seed deterministisch aus der Kameraposition (NICHT hash() -> PYTHONHASHSEED), damit
+# dieselbe Kamera über alle Posen denselben Kalibrierfehler trägt.
+if intr_focal_err_pct > 0.0 or intr_principal_px > 0.0 or intr_residual_dist:
+    _cp = rendering_info.get("cameraPosition", [0, 0, 0])
+    _seed = abs(int(round(float(_cp[0]) * 1000 + float(_cp[1]) * 100 + float(_cp[2]) * 10))) + 17
+    _rng = random.Random(_seed)
+    if intr_focal_err_pct > 0.0:
+        fx *= 1.0 + _rng.uniform(-1.0, 1.0) * intr_focal_err_pct / 100.0
+        fy *= 1.0 + _rng.uniform(-1.0, 1.0) * intr_focal_err_pct / 100.0
+    if intr_principal_px > 0.0:
+        cx += _rng.uniform(-1.0, 1.0) * intr_principal_px
+        cy += _rng.uniform(-1.0, 1.0) * intr_principal_px
+    camera_matrix = np.array([[fx, 0, cx], [0, fy, cy], [0, 0, 1]], dtype=np.float32)
+    if intr_residual_dist:
+        _k = [float(v) for v in intr_residual_dist]
+        dist_coeffs = np.array([[_k[0] if len(_k) > 0 else 0.0],
+                                [_k[1] if len(_k) > 1 else 0.0],
+                                [0.0], [0.0], [0.0]], dtype=np.float32)
+    print(f"[render] (A) intrinsics jitter -> fx={fx:.1f} fy={fy:.1f} cx={cx:.1f} cy={cy:.1f} "
+          f"dist={dist_coeffs.ravel()[:2]}")
+
 np.savez(
     CALIBRATION_OUTPUT,
 
@@ -1045,27 +1086,127 @@ enable_best_device(scene)
 # RENDER
 # ============================================================
 
-# ── leichtes Verwackeln: pro Aufnahme zufällig gerichteter kleiner Blur ──
-if motion_blur and motion_blur_max_px > 0.0:
+# ── Post-Processing: Verwackeln + Linsen-/Sensor-Effekte (B-D) ──
+# Eine Compositor-Kette; jeder Effekt einzeln gekapselt, damit ein API-Problem den
+# Render nicht abbricht (der betroffene Effekt wird dann nur übersprungen + gewarnt).
+_post_active = ((motion_blur and motion_blur_max_px > 0.0) or lens_distortion or
+                localized_blur or vignette or lens_dirt or sensor_noise)
+if _post_active:
     scene.use_nodes = True
     tree = scene.node_tree
     for _n in list(tree.nodes):
         tree.nodes.remove(_n)
-    rl   = tree.nodes.new("CompositorNodeRLayers")
-    blur = tree.nodes.new("CompositorNodeBlur")
+    rl = tree.nodes.new("CompositorNodeRLayers")
+    cur = rl.outputs["Image"]
+
+    # (D) chromatische Aberration (Dispersion) — KEINE geometrische Verzeichnung (-> A/npz)
+    if lens_distortion and lens_distortion_strength > 0.0:
+        try:
+            ld = tree.nodes.new("CompositorNodeLensdist")
+            ld.use_projector = False
+            ld.inputs["Distort"].default_value = 0.0
+            ld.inputs["Dispersion"].default_value = min(1.0, lens_distortion_strength)
+            tree.links.new(cur, ld.inputs["Image"]); cur = ld.outputs["Image"]
+            print("[render] (D) chromatic aberration")
+        except Exception as _e:
+            print("[render][WARN] lens_distortion skipped:", _e)
+
+    # Verwackeln (Motion Blur), pro Aufnahme zufällig gerichtet
+    if motion_blur and motion_blur_max_px > 0.0:
+        try:
+            mb = tree.nodes.new("CompositorNodeBlur")
+            mb.filter_type = "GAUSS"; mb.use_relative = False
+            _ang = random.uniform(0.0, math.pi)
+            _amp = random.uniform(0.35, 1.0) * motion_blur_max_px
+            mb.size_x = max(0, int(round(abs(_amp * math.cos(_ang)))))
+            mb.size_y = max(0, int(round(abs(_amp * math.sin(_ang)))))
+            tree.links.new(cur, mb.inputs["Image"]); cur = mb.outputs["Image"]
+            print(f"[render] motion blur size=({mb.size_x},{mb.size_y})")
+        except Exception as _e:
+            print("[render][WARN] motion_blur skipped:", _e)
+
+    # (C) Rand-Unschärfe (Feldkrümmung): scharf in der Mitte, unscharf am Rand
+    if localized_blur and localized_blur_strength > 0.0:
+        try:
+            eb = tree.nodes.new("CompositorNodeBlur")
+            eb.filter_type = "GAUSS"; eb.use_relative = True
+            eb.factor_x = min(0.5, localized_blur_strength)
+            eb.factor_y = min(0.5, localized_blur_strength)
+            em = tree.nodes.new("CompositorNodeEllipseMask")
+            em.width = 0.7; em.height = 0.7
+            ex = tree.nodes.new("CompositorNodeMixRGB")
+            tree.links.new(cur, eb.inputs["Image"])
+            tree.links.new(em.outputs["Mask"], ex.inputs["Fac"])
+            tree.links.new(eb.outputs["Image"], ex.inputs[1])   # Fac=0 (Rand) -> Blur
+            tree.links.new(cur, ex.inputs[2])                   # Fac=1 (Mitte) -> scharf
+            cur = ex.outputs["Image"]
+            print("[render] (C) edge blur")
+        except Exception as _e:
+            print("[render][WARN] localized_blur skipped:", _e)
+
+    # (C) Vignette: Randabdunklung über weiche Ellipse-Maske
+    if vignette and vignette_strength > 0.0:
+        try:
+            vm = tree.nodes.new("CompositorNodeEllipseMask")
+            vm.width = 1.0; vm.height = 1.0
+            vs = tree.nodes.new("CompositorNodeBlur")
+            vs.filter_type = "GAUSS"; vs.use_relative = True
+            vs.factor_x = 0.3; vs.factor_y = 0.3
+            vr = tree.nodes.new("CompositorNodeMapRange")
+            vr.inputs["From Min"].default_value = 0.0
+            vr.inputs["From Max"].default_value = 1.0
+            vr.inputs["To Min"].default_value = 1.0 - min(0.9, vignette_strength)
+            vr.inputs["To Max"].default_value = 1.0
+            vmul = tree.nodes.new("CompositorNodeMixRGB")
+            vmul.blend_type = "MULTIPLY"; vmul.inputs["Fac"].default_value = 1.0
+            tree.links.new(vm.outputs["Mask"], vs.inputs["Image"])
+            tree.links.new(vs.outputs["Image"], vr.inputs["Value"])
+            tree.links.new(cur, vmul.inputs[1])
+            tree.links.new(vr.outputs["Value"], vmul.inputs[2])
+            cur = vmul.outputs["Image"]
+            print("[render] (C) vignette")
+        except Exception as _e:
+            print("[render][WARN] vignette skipped:", _e)
+
+    # (B) Staub auf der Linse: wenige dunkle Flecken über dem ganzen Bild
+    if lens_dirt and lens_dirt_strength > 0.0:
+        try:
+            dtex = bpy.data.textures.new("lensDirtTex", type="VORONOI")
+            dt = tree.nodes.new("CompositorNodeTexture")
+            dt.texture = dtex
+            dramp = tree.nodes.new("CompositorNodeValToRGB")
+            dramp.color_ramp.elements[0].position = 0.0
+            dramp.color_ramp.elements[1].position = 0.12   # nur wenige Flecken
+            dramp.color_ramp.elements[0].color = (1, 1, 1, 1)
+            dramp.color_ramp.elements[1].color = (1.0 - min(0.9, lens_dirt_strength),) * 3 + (1.0,)
+            dmul = tree.nodes.new("CompositorNodeMixRGB")
+            dmul.blend_type = "MULTIPLY"; dmul.inputs["Fac"].default_value = 1.0
+            tree.links.new(dt.outputs["Value"], dramp.inputs["Fac"])
+            tree.links.new(cur, dmul.inputs[1])
+            tree.links.new(dramp.outputs["Image"], dmul.inputs[2])
+            cur = dmul.outputs["Image"]
+            print("[render] (B) lens dirt")
+        except Exception as _e:
+            print("[render][WARN] lens_dirt skipped:", _e)
+
+    # (D) Sensor-Rauschen: feines additives Rauschen
+    if sensor_noise and sensor_noise_strength > 0.0:
+        try:
+            ntex = bpy.data.textures.new("sensorNoiseTex", type="NOISE")
+            nt = tree.nodes.new("CompositorNodeTexture")
+            nt.texture = ntex
+            nmix = tree.nodes.new("CompositorNodeMixRGB")
+            nmix.blend_type = "ADD"
+            nmix.inputs["Fac"].default_value = min(0.3, sensor_noise_strength)
+            tree.links.new(cur, nmix.inputs[1])
+            tree.links.new(nt.outputs["Image"], nmix.inputs[2])
+            cur = nmix.outputs["Image"]
+            print("[render] (D) sensor noise")
+        except Exception as _e:
+            print("[render][WARN] sensor_noise skipped:", _e)
+
     comp = tree.nodes.new("CompositorNodeComposite")
-    blur.filter_type = "GAUSS"
-    blur.use_relative = False
-    _ang = random.uniform(0.0, math.pi)               # zufällige Verwackel-Richtung
-    _amp = random.uniform(0.35, 1.0) * motion_blur_max_px
-    blur.size_x = max(0, int(round(abs(_amp * math.cos(_ang)))))
-    blur.size_y = max(0, int(round(abs(_amp * math.sin(_ang)))))
-    rl.location   = (-300, 0)
-    blur.location = (0, 0)
-    comp.location = (300, 0)
-    tree.links.new(rl.outputs["Image"], blur.inputs["Image"])
-    tree.links.new(blur.outputs["Image"], comp.inputs["Image"])
-    print(f"[render] motion blur size=({blur.size_x},{blur.size_y}) px")
+    tree.links.new(cur, comp.inputs["Image"])
 
 bpy.ops.render.render(write_still=True)
 print("Finished rendering:", OUTPUT_FILE)
diff --git a/setup/generateSets/render_robot_5_1.py b/setup/generateSets/render_robot_5_1.py
deleted file mode 100644
index 855d09e..0000000
--- a/setup/generateSets/render_robot_5_1.py
+++ /dev/null
@@ -1,1045 +0,0 @@
-from unicodedata import name
-
-import bpy
-import math
-import mathutils
-import json
-from pathlib import Path
-from typing import Any, Dict, Iterable, List, Optional, Tuple
-from mathutils import Matrix
-
-# ============================================================
-# PATHS
-# ============================================================
-
-ROBOT_JSON_FILE = r"C:\Users\kech\SynologyDrive\2026-AppServer-AppRobot\appRobotRendering\data\robot\robot.json"
-OUTPUT_FILE = r"C:\Users\kech\SynologyDrive\2026-AppServer-AppRobot\appRobotRendering\data\simulation\debug\render.png"
-
-# ============================================================
-# DEFAULT MATERIALS
-# ============================================================
-
-DEFAULT_MATERIALS = {
-    "wood": {"baseColor": (0.72, 0.52, 0.33, 1.0), "roughness": 0.85, "metallic": 0.0},
-    "plaWhite": {"baseColor": (0.95, 0.95, 0.95, 1.0), "roughness": 0.45, "metallic": 0.0},
-    "steel": {"baseColor": (0.72, 0.72, 0.75, 1.0), "roughness": 0.25, "metallic": 1.0},
-    "powderCoatBlue": {"baseColor": (0.15, 0.25, 0.70, 1.0), "roughness": 0.55, "metallic": 0.0},
-    "defaultPlastic": {"baseColor": (0.95, 0.95, 0.95, 1.0), "roughness": 0.40, "metallic": 0.0},
-    "skeletonRed": {"baseColor": (0.85, 0.20, 0.20, 1.0), "roughness": 0.35, "metallic": 0.0},
-    "markerBlack": {"baseColor": (0.04, 0.04, 0.04, 1.0), "roughness": 0.80, "metallic": 0.0},
-}
-
-STATE_KEYS = ["x", "y", "z", "a", "b", "c", "e"]
-
-marker_export = []
-
-
-print("2 LOADING")
-# ============================================================
-# JSON LOADING
-# ============================================================
-
-with open(ROBOT_JSON_FILE, "r", encoding="utf-8") as f:
-    robot: Dict[str, Any] = json.load(f)
-
-rendering_info = robot.get("renderingInfo", {})
-metric = rendering_info.get("metric", "mm")
-scale_factor = 0.001 if metric == "mm" else 1.0
-
-RENDER_WIDTH = int(rendering_info.get("width", 1200))
-RENDER_HEIGHT = int(rendering_info.get("height", 800))
-
-def as_bool(value: Any, default: bool = False) -> bool:
-    if value is None:
-        return default
-    if isinstance(value, bool):
-        return value
-    if isinstance(value, str):
-        return value.strip().lower() in ("1", "true", "yes", "on")
-    return bool(value)
-
-show_skeleton = as_bool(rendering_info.get("showSkeleton", False))
-show_markers = as_bool(rendering_info.get("showMarkers", False))
-
-lens_dirt = as_bool(rendering_info.get("lensDirt", False))
-lens_dirt_strength = float(rendering_info.get("lensDirtStrength", 0.08))
-
-dof_enabled = as_bool(rendering_info.get("dofEnabled", True))
-dof_fstop = float(rendering_info.get("dofFStop", 7.5))
-
-aruco_dust = as_bool(rendering_info.get("arucoDust", False))
-aruco_dust_strength = float(rendering_info.get("arucoDustStrength", 0.0005))
-
-state: Dict[str, float] = {k: 0.0 for k in STATE_KEYS}
-for source_name in ("defaultPosition", "recognized", "movements"):
-    source = robot.get(source_name, {}) or {}
-    for k in STATE_KEYS:
-        v = source.get(k, None)
-        if v is not None:
-            try:
-                state[k] = float(v)
-            except Exception:
-                pass
-
-links_def = robot.get("links", {})
-if not isinstance(links_def, dict):
-    raise ValueError("robot.json must contain a top-level 'links' object")
-
-
-
-# ============================================================
-# HELPERS
-# ============================================================
-
-def mm_to_m(value: float) -> float:
-    return value * scale_factor
-
-def resolve_scalar(value: Any, state_map: Dict[str, float]) -> float:
-    if value is None:
-        return 0.0
-    if isinstance(value, (int, float)):
-        return float(value)
-    if isinstance(value, str):
-        key = value.strip().lower()
-        if key in state_map:
-            return float(state_map[key])
-        try:
-            return float(key)
-        except ValueError:
-            return 0.0
-    return 0.0
-
-def resolve_vector(value: Any, state_map: Dict[str, float], default_len: int = 3) -> Tuple[float, ...]:
-    if value is None:
-        return tuple(0.0 for _ in range(default_len))
-    if isinstance(value, (int, float, str)):
-        return (resolve_scalar(value, state_map),)
-    if isinstance(value, (list, tuple)):
-        resolved = [resolve_scalar(v, state_map) for v in value]
-        if len(resolved) < default_len:
-            resolved.extend([0.0] * (default_len - len(resolved)))
-        return tuple(resolved[:default_len])
-    return tuple(0.0 for _ in range(default_len))
-
-def resolve_vec3_m(value: Any, state_map: Dict[str, float]) -> Tuple[float, float, float]:
-    vec = list(resolve_vector(value, state_map, default_len=3))
-    while len(vec) < 3:
-        vec.append(0.0)
-    x, y, z = vec[:3]
-    return mm_to_m(x), mm_to_m(y), mm_to_m(z)
-
-def normalize_axis(axis: Iterable[Any]) -> mathutils.Vector:
-    ax = mathutils.Vector((float(axis[0]), float(axis[1]), float(axis[2])))
-    return ax.normalized() if ax.length > 0 else mathutils.Vector((1.0, 0.0, 0.0))
-
-def euler_deg_xyz(values: Any) -> Tuple[float, float, float]:
-    vec = list(resolve_vector(values, state, default_len=3))
-    while len(vec) < 3:
-        vec.append(0.0)
-    return math.radians(vec[0]), math.radians(vec[1]), math.radians(vec[2])
-
-def create_or_get_material(name: str, fallback: str = "defaultPlastic") -> bpy.types.Material:
-    info = rendering_info.get("materials", {}) or {}
-    spec = None
-
-    if isinstance(info, dict):
-        spec = info.get(name)
-
-    if isinstance(spec, dict):
-        base = DEFAULT_MATERIALS.get(name, DEFAULT_MATERIALS[fallback]).copy()
-        if "baseColor" in spec:
-            color = tuple(spec["baseColor"])
-            base["baseColor"] = (*color[:3], 1.0) if len(color) == 3 else tuple(color[:4])
-        if "roughness" in spec:
-            base["roughness"] = float(spec["roughness"])
-        if "metallic" in spec:
-            base["metallic"] = float(spec["metallic"])
-        spec = base
-    else:
-        spec = DEFAULT_MATERIALS.get(name, DEFAULT_MATERIALS[fallback])
-
-    if name in bpy.data.materials:
-        mat = bpy.data.materials[name]
-    else:
-        mat = bpy.data.materials.new(name=name)
-
-    mat.use_nodes = True
-    bsdf = mat.node_tree.nodes.get("Principled BSDF")
-    if bsdf is not None:
-        bsdf.inputs["Base Color"].default_value = spec["baseColor"]
-        bsdf.inputs["Roughness"].default_value = spec["roughness"]
-        bsdf.inputs["Metallic"].default_value = spec["metallic"]
-
-    if name == "wood" and mat.node_tree.nodes.get("WoodGrainMix") is None:
-        nodes = mat.node_tree.nodes
-        links = mat.node_tree.links
-
-        texcoord = nodes.new("ShaderNodeTexCoord")
-        mapping = nodes.new("ShaderNodeMapping")
-        noise = nodes.new("ShaderNodeTexNoise")
-        wave = nodes.new("ShaderNodeTexWave")
-        ramp = nodes.new("ShaderNodeValToRGB")
-        mix = nodes.new("ShaderNodeMixRGB")
-
-        texcoord.name = "WoodTexCoord"
-        mapping.name = "WoodMapping"
-        noise.name = "WoodNoise"
-        wave.name = "WoodWave"
-        ramp.name = "WoodRamp"
-        mix.name = "WoodGrainMix"
-
-        texcoord.location = (-900, 0)
-        mapping.location = (-700, 0)
-        noise.location = (-700, -220)
-        wave.location = (-500, 0)
-        ramp.location = (-260, 0)
-        mix.location = (-60, 0)
-
-        # statt extrem hoch:
-        mapping.inputs["Scale"].default_value = (1000.0, 1000.0, 1000.0)
-        wave.inputs["Scale"].default_value = 1.0
-        noise.inputs["Scale"].default_value = 6.0
-        wave.inputs["Distortion"].default_value = 3.0
-
-        # und wichtig: Wave Fac statt Color verwenden
-        links.new(wave.outputs["Fac"], ramp.inputs["Fac"])
-
-        bump = nodes.new("ShaderNodeBump")
-        bump.location = (120, -160)
-        bump.inputs["Strength"].default_value = 0.4
-
-        links.new(ramp.outputs["Color"], bump.inputs["Height"])
-        links.new(bump.outputs["Normal"], bsdf.inputs["Normal"])
-
-        ramp.color_ramp.elements[0].position = 0.53
-        ramp.color_ramp.elements[1].position = 0.58
-
-        mix.blend_type = "MIX"
-        mix.inputs["Fac"].default_value = 0.08
-        mix.inputs["Color1"].default_value = spec["baseColor"]
-        mix.inputs["Color2"].default_value = (0.74, 0.58, 0.50, 1.0)
-
-
-        links.new(texcoord.outputs["Object"], mapping.inputs["Vector"])
-        links.new(mapping.outputs["Vector"], noise.inputs["Vector"])
-        links.new(noise.outputs["Fac"], wave.inputs["Distortion"])
-        links.new(mapping.outputs["Vector"], wave.inputs["Vector"])
-        links.new(wave.outputs["Color"], ramp.inputs["Fac"])
-        links.new(ramp.outputs["Color"], mix.inputs["Fac"])
-        links.new(mix.outputs["Color"], bsdf.inputs["Base Color"])
-                
-    return mat
-
-def import_stl(filepath: str) -> List[bpy.types.Object]:
-    path = Path(filepath).resolve()
-
-    if not path.exists():
-        raise FileNotFoundError(path)
-
-    before = {obj.name for obj in bpy.data.objects}
-
-    try:
-        bpy.ops.wm.stl_import(filepath=str(path))
-    except Exception as e:
-        raise RuntimeError(f"STL import failed: {path}") from e
-
-    imported = [
-        obj
-        for obj in bpy.data.objects
-        if obj.name not in before
-    ]
-
-    return imported
-
-def create_empty(name: str) -> bpy.types.Object:
-    empty = bpy.data.objects.new(name, None)
-    bpy.context.collection.objects.link(empty)
-    return empty
-
-def safe_parent(child: bpy.types.Object, parent: Optional[bpy.types.Object], keep_world: bool = False):
-    if parent is None:
-        return
-    world_matrix = child.matrix_world.copy()
-    child.parent = parent
-    if keep_world:
-        child.matrix_parent_inverse = parent.matrix_world.inverted()
-        child.matrix_world = world_matrix
-    else:
-        child.matrix_parent_inverse = Matrix.Identity(4)
-
-def create_material_segment(name: str, color: Tuple[float, float, float], roughness: float = 0.35) -> bpy.types.Material:
-    if name in bpy.data.materials:
-        mat = bpy.data.materials[name]
-    else:
-        mat = bpy.data.materials.new(name=name)
-    mat.use_nodes = True
-    bsdf = mat.node_tree.nodes.get("Principled BSDF")
-    if bsdf is not None:
-        bsdf.inputs["Base Color"].default_value = (color[0], color[1], color[2], 1.0)
-        bsdf.inputs["Roughness"].default_value = roughness
-        bsdf.inputs["Metallic"].default_value = 0.0
-    return mat
-
-def create_cylinder_between(
-    name: str,
-    p1_local: Tuple[float, float, float],
-    p2_local: Tuple[float, float, float],
-    radius_m: float,
-    parent: bpy.types.Object,
-    material: bpy.types.Material
-) -> bpy.types.Object:
-    v1 = mathutils.Vector(p1_local)
-    v2 = mathutils.Vector(p2_local)
-    delta = v2 - v1
-    length = delta.length
-    if length <= 1e-9:
-        length = 1e-6
-        delta = mathutils.Vector((0.0, 0.0, 1e-6))
-
-    bpy.ops.mesh.primitive_cylinder_add(radius=radius_m, depth=length)
-    obj = bpy.context.active_object
-    obj.name = name
-    safe_parent(obj, parent, keep_world=False)
-    obj.location = (v1 + v2) * 0.5
-    obj.rotation_mode = "QUATERNION"
-    obj.rotation_quaternion = mathutils.Vector((0, 0, 1)).rotation_difference(delta.normalized())
-    if len(obj.data.materials) == 0:
-        obj.data.materials.append(material)
-    else:
-        obj.data.materials[0] = material
-    return obj
-
-def derive_default_skeleton_from_size(size_mm: List[float]) -> Dict[str, Any]:
-    sx, sy, sz = (float(size_mm[0]), float(size_mm[1]), float(size_mm[2]))
-    ax = max((abs(sx), 0), (abs(sy), 1), (abs(sz), 2), key=lambda x: x[0])[1]
-
-    if ax == 0:
-        return {"from": [0, sy * 0.5, sz * 0.5], "to": [sx, sy * 0.5, sz * 0.5]}
-    if ax == 1:
-        return {"from": [sx * 0.5, 0, sz * 0.5], "to": [sx * 0.5, sy, sz * 0.5]}
-    return {"from": [sx * 0.5, sy * 0.5, 0], "to": [sx * 0.5, sy * 0.5, sz]}
-
-def resolve_stl_path(stl_file: str) -> Path:
-    base_dir = Path(ROBOT_JSON_FILE).parent
-    candidates = [
-        base_dir / stl_file,
-        base_dir / "surfaces" / stl_file,
-        Path(stl_file),
-    ]
-    for c in candidates:
-        p = c.resolve()
-        if p.exists():
-            return p
-    raise FileNotFoundError(
-        "STL file not found in any expected location:\n" +
-        "\n".join(str(c.resolve()) for c in candidates)
-    )
-
-print("3 SETUP")
-# ============================================================
-# SCENE RESET
-# ============================================================
-
-
-for obj in list(bpy.data.objects):
-    bpy.data.objects.remove(obj, do_unlink=True)
-
-scene = bpy.context.scene
-scene.unit_settings.system = "METRIC"
-scene.unit_settings.length_unit = "MILLIMETERS"
-scene.unit_settings.scale_length = scale_factor
-
-# ============================================================
-# WORLD / RENDER SETTINGS
-# ============================================================
-
-world = scene.world or bpy.data.worlds.new("World")
-scene.world = world
-world.use_nodes = True
-bg = world.node_tree.nodes["Background"]
-bg.inputs[0].default_value = tuple(rendering_info.get("backgroundColor", [0.70, 0.85, 1.0])) + (1.0,)
-bg.inputs[1].default_value = float(rendering_info.get("backgroundStrength", 0.20))
-
-scene.render.engine = "CYCLES"
-scene.view_settings.exposure = float(rendering_info.get("exposure", -1.5))
-scene.cycles.samples = 32
-scene.cycles.preview_samples = 32
-scene.cycles.use_adaptive_sampling = True
-scene.cycles.adaptive_threshold = 0.02
-scene.cycles.use_denoising = True
-scene.render.resolution_x = RENDER_WIDTH
-scene.render.resolution_y = RENDER_HEIGHT
-scene.render.resolution_percentage = 100
-scene.render.image_settings.file_format = "PNG"
-scene.render.filepath = OUTPUT_FILE
-scene.render.film_transparent = False
-
-print("4 SCENE")
-# ============================================================
-# FLOOR
-# ============================================================
-
-mesh = bpy.data.meshes.new("FloorMesh")
-
-verts = [
-    (-1,-1,0),
-    ( 1,-1,0),
-    ( 1, 1,0),
-    (-1, 1,0),
-]
-
-faces = [(0,1,2,3)]
-
-mesh.from_pydata(verts, [], faces)
-mesh.update()
-
-floor = bpy.data.objects.new("Floor", mesh)
-
-bpy.context.collection.objects.link(floor)
-
-floor.location = (
-    0,
-    0,
-    mm_to_m(-28.0)
-)
-
-floor_tex_path = Path(ROBOT_JSON_FILE).parent / "surfaces" / "boden.jpg"
-
-if floor_tex_path.exists():
-    floor_mat = bpy.data.materials.new(name="FloorPhoto")
-    floor_mat.use_nodes = True
-
-    nodes = floor_mat.node_tree.nodes
-    links = floor_mat.node_tree.links
-    nodes.clear()
-
-    output_node = nodes.new(type="ShaderNodeOutputMaterial")
-    bsdf_node = nodes.new(type="ShaderNodeBsdfPrincipled")
-    texcoord_node = nodes.new(type="ShaderNodeTexCoord")
-    mapping_node = nodes.new(type="ShaderNodeMapping")
-    image_node = nodes.new(type="ShaderNodeTexImage")
-
-    image_node.image = bpy.data.images.load(str(floor_tex_path), check_existing=True)
-    image_node.interpolation = "Cubic"
-
-    links.new(texcoord_node.outputs["UV"], mapping_node.inputs["Vector"])
-    links.new(mapping_node.outputs["Vector"], image_node.inputs["Vector"])
-    links.new(image_node.outputs["Color"], bsdf_node.inputs["Base Color"])
-    links.new(bsdf_node.outputs["BSDF"], output_node.inputs["Surface"])
-
-    floor.data.materials.append(floor_mat)
-else:
-    # Fallback: dein bisheriges Checkerboard behalten
-    checker_mat = bpy.data.materials.new(name="Checkerboard")
-    checker_mat.use_nodes = True
-    nodes = checker_mat.node_tree.nodes
-    links = checker_mat.node_tree.links
-    nodes.clear()
-
-    output_node = nodes.new(type="ShaderNodeOutputMaterial")
-    bsdf_node = nodes.new(type="ShaderNodeBsdfPrincipled")
-    checker_node = nodes.new(type="ShaderNodeTexChecker")
-    mapping_node = nodes.new(type="ShaderNodeMapping")
-    texcoord_node = nodes.new(type="ShaderNodeTexCoord")
-
-    checker_node.inputs["Color1"].default_value = (0.82, 0.82, 0.82, 1.0)
-    checker_node.inputs["Color2"].default_value = (0.18, 0.18, 0.18, 1.0)
-    mapping_node.inputs["Scale"].default_value = (20.0, 20.0, 20.0)
-
-    links.new(texcoord_node.outputs["UV"], mapping_node.inputs["Vector"])
-    links.new(mapping_node.outputs["Vector"], checker_node.inputs["Vector"])
-    links.new(checker_node.outputs["Color"], bsdf_node.inputs["Base Color"])
-    links.new(bsdf_node.outputs["BSDF"], output_node.inputs["Surface"])
-    floor.data.materials.append(checker_mat)
-
-# ============================================================
-# CAMERA
-# ============================================================
-
-cam_data = bpy.data.cameras.new("Camera")
-cam_obj = bpy.data.objects.new("Camera", cam_data)
-bpy.context.collection.objects.link(cam_obj)
-
-cam_pos = resolve_vec3_m(rendering_info.get("cameraPosition", [-400, -700, 300]), state)
-cam_target = resolve_vec3_m(rendering_info.get("cameraTarget", [0, 0, 0]), state)
-cam_obj.location = cam_pos
-cam_data.lens = 50
-
-if dof_enabled:
-    cam_data.dof.use_dof = True
-    cam_data.dof.focus_distance = (mathutils.Vector(cam_target) - mathutils.Vector(cam_pos)).length
-    cam_data.dof.aperture_fstop = dof_fstop
-else:
-    cam_data.dof.use_dof = False
-
-
-cam_vec = mathutils.Vector(cam_target) - mathutils.Vector(cam_pos)
-if cam_vec.length == 0:
-    cam_vec = mathutils.Vector((1, 0, 0))
-cam_obj.rotation_euler = cam_vec.to_track_quat("-Z", "Y").to_euler()
-scene.camera = cam_obj
-
-print("5 CAMERA")
-# ============================================================
-# EXPORT CAMERA CALIBRATION (.npz)
-# ============================================================
-
-import numpy as np
-
-CALIBRATION_OUTPUT = str(
-    Path(OUTPUT_FILE).with_suffix(".npz")
-)
-
-render = scene.render
-cam = cam_obj.data
-
-width_px = render.resolution_x
-height_px = render.resolution_y
-
-scale = render.resolution_percentage / 100.0
-
-width_px *= scale
-height_px *= scale
-
-sensor_width_mm = cam.sensor_width
-sensor_height_mm = cam.sensor_height
-
-focal_mm = cam.lens
-
-# focal length in pixels
-fx = (width_px * focal_mm) / sensor_width_mm
-fy = (height_px * focal_mm) / sensor_height_mm
-
-cx = width_px / 2.0
-cy = height_px / 2.0
-
-camera_matrix = np.array([
-    [fx, 0,  cx],
-    [0,  fy, cy],
-    [0,  0,   1]
-], dtype=np.float32)
-
-# ideal synthetic camera
-dist_coeffs = np.zeros((5, 1), dtype=np.float32)
-
-np.savez(
-    CALIBRATION_OUTPUT,
-
-    # common names
-    camera_matrix=camera_matrix,
-    dist_coeffs=dist_coeffs,
-
-    # compatibility aliases
-    K=camera_matrix,
-    mtx=camera_matrix,
-    dist=dist_coeffs
-)
-
-print("Saved camera calibration:", CALIBRATION_OUTPUT)
-print(camera_matrix)
-
-
-print("6 LIGHTS")
-# ============================================================
-# LIGHTS
-# ============================================================
-
-sun_data = bpy.data.lights.new(name="Sun", type="SUN")
-sun_obj = bpy.data.objects.new(name="Sun", object_data=sun_data)
-bpy.context.collection.objects.link(sun_obj)
-sun_pos = resolve_vec3_m(rendering_info.get("lightPosition", [-500, -500, 500]), state)
-light_target = resolve_vec3_m(rendering_info.get("lightTarget", [0, 0, 0]), state)
-sun_obj.location = sun_pos
-light_vec = mathutils.Vector(light_target) - mathutils.Vector(sun_pos)
-if light_vec.length == 0:
-    light_vec = mathutils.Vector((1, 0, -1))
-sun_obj.rotation_euler = light_vec.to_track_quat("-Z", "Y").to_euler()
-sun_data.energy = float(rendering_info.get("sunEnergy", 0.35))
-
-area_data = bpy.data.lights.new(name="AreaLight", type="AREA")
-area_obj = bpy.data.objects.new(name="AreaLight", object_data=area_data)
-bpy.context.collection.objects.link(area_obj)
-area_obj.location = (mm_to_m(-800), mm_to_m(-1200), mm_to_m(1500))
-area_obj.rotation_euler = (math.radians(60), 0.0, math.radians(-20))
-area_data.energy = float(rendering_info.get("areaEnergy", 120))
-area_data.size = 2.0
-
-# ============================================================
-# ROBOT HIERARCHY
-# ============================================================
-
-link_frames: Dict[str, bpy.types.Object] = {}
-for link_name in links_def.keys():
-    link_frames[link_name] = create_empty(f"{link_name}_frame")
-
-for link_name, link_info in links_def.items():
-    parent_name = link_info.get("parent")
-    parent_frame = link_frames.get(parent_name) if parent_name else None
-    size_mm = link_info.get("size", [100, 100, 100])
-
-    # mount: static position/rotation in parent coordinates
-    mount = create_empty(f"{link_name}_mount")
-    safe_parent(mount, parent_frame, keep_world=False)
-    mount.location = resolve_vec3_m(link_info.get("mountPosition", [0, 0, 0]), state)
-    mount.rotation_euler = euler_deg_xyz(link_info.get("mountRotation", [0, 0, 0]))
-
-    # joint: sits inside the mount, defines pivot/orientation
-    joint_info = link_info.get("jointToParent", {}) or {}
-    joint = create_empty(f"{link_name}_joint")
-    safe_parent(joint, mount, keep_world=False)
-    joint.location = resolve_vec3_m(joint_info.get("origin", [0, 0, 0]), state)
-    joint.rotation_euler = euler_deg_xyz(joint_info.get("rotation", [0, 0, 0]))
-
-    # motion: only this node gets the commanded position/angle
-    motion = create_empty(f"{link_name}_motion")
-    safe_parent(motion, joint, keep_world=False)
-
-    joint_type = str(joint_info.get("type", "fixed")).lower()
-    control_var = str(joint_info.get("variable", joint_info.get("control", ""))).lower()
-    axis = joint_info.get("axis", [1, 0, 0])
-
-    if joint_type == "linear":
-        value_mm = state.get(control_var, 0.0) if control_var else 0.0
-        motion.location = normalize_axis(axis) * mm_to_m(value_mm)
-    elif joint_type == "revolute":
-        value_deg = state.get(control_var, 0.0) if control_var else 0.0
-        motion.rotation_mode = "QUATERNION"
-        motion.rotation_quaternion = mathutils.Quaternion(normalize_axis(axis), math.radians(value_deg))
-
-    # link frame: everything belonging to this link follows motion
-    link_frame = link_frames[link_name]
-    safe_parent(link_frame, motion, keep_world=False)
-
-    # --------------------------------------------------------
-    # VISUAL MESHES
-    # --------------------------------------------------------
-
-    visual_root = create_empty(f"{link_name}_visual")
-    safe_parent(visual_root, link_frame, keep_world=False)
-
-    model_list = link_info.get("model", [])
-    if not isinstance(model_list, list):
-        model_list = []
-
-    for idx, model_def in enumerate(model_list):
-        stl_file = model_def.get("stlFile")
-        if not stl_file:
-            continue
-
-        stl_path = resolve_stl_path(stl_file)
-        imported = import_stl(str(stl_path))
-
-        model_node = create_empty(f"{link_name}_model_{idx}")
-        safe_parent(model_node, visual_root, keep_world=False)
-        model_node.location = resolve_vec3_m(model_def.get("originOfModel", [0, 0, 0]), state)
-        model_node.rotation_euler = euler_deg_xyz(model_def.get("rotationOfModelDegree", [0, 0, 0]))
-
-        material_name = model_def.get("material", "defaultPlastic")
-        material = create_or_get_material(material_name)
-
-        for obj in imported:
-            if obj.type != "MESH":
-                continue
-            safe_parent(obj, model_node, keep_world=True)
-            obj.scale = (scale_factor, scale_factor, scale_factor)
-            if len(obj.data.materials) == 0:
-                obj.data.materials.append(material)
-            else:
-                obj.data.materials[0] = material
-
-    # --------------------------------------------------------
-    # SKELETON DEBUG
-    # --------------------------------------------------------
-
-    if show_skeleton:
-        skeleton_spec = link_info.get("skeleton")
-        if not isinstance(skeleton_spec, dict):
-            skeleton_spec = derive_default_skeleton_from_size(size_mm)
-
-        p1_mm = skeleton_spec.get("from", [0, 0, 0])
-        p2_mm = skeleton_spec.get("to", [0, 0, 0])
-        p1 = resolve_vec3_m(p1_mm, state)
-        p2 = resolve_vec3_m(p2_mm, state)
-
-        sk_radius_mm = float(
-            skeleton_spec.get(
-                "radius",
-                rendering_info.get("skeletonDefaults", {}).get("radius", 4)
-            )
-        )
-        sk_color = skeleton_spec.get(
-            "color",
-            rendering_info.get("skeletonDefaults", {}).get("color", [0.85, 0.20, 0.20])
-        )
-        sk_mat = create_material_segment(f"{link_name}_skeletonMat", tuple(sk_color[:3]))
-
-        create_cylinder_between(
-            f"{link_name}_skeleton",
-            p1,
-            p2,
-            mm_to_m(sk_radius_mm),
-            link_frame,
-            sk_mat
-        )
-
-    # --------------------------------------------------------
-    # MARKERS
-    # --------------------------------------------------------
-
-    import cv2
-    import numpy as np
-    import tempfile
-
-    def create_aruco_material(marker_id: int, marker_name: str):
-        dictionary = cv2.aruco.getPredefinedDictionary(cv2.aruco.DICT_4X4_250)
-
-        img = np.zeros((256, 256), dtype=np.uint8)
-        cv2.aruco.generateImageMarker(dictionary, marker_id, 256, img, 1)
-
-        tmpfile = Path(tempfile.gettempdir()) / f"aruco_{marker_id}.png"
-        cv2.imwrite(str(tmpfile), img)
-
-        image = bpy.data.images.load(str(tmpfile))
-
-        mat = bpy.data.materials.new(name=f"{marker_name}_mat")
-        mat.use_nodes = True
-
-        nodes = mat.node_tree.nodes
-        links = mat.node_tree.links
-        nodes.clear()
-
-        out = nodes.new(type="ShaderNodeOutputMaterial")
-        bsdf = nodes.new(type="ShaderNodeBsdfPrincipled")
-        tex = nodes.new(type="ShaderNodeTexImage")
-
-        tex.image = image
-
-        if aruco_dust:
-            noise = nodes.new("ShaderNodeTexNoise")
-            ramp = nodes.new("ShaderNodeValToRGB")
-            mix = nodes.new("ShaderNodeMixRGB")
-
-            noise.location = (-600, -220)
-            ramp.location = (-360, -220)
-            mix.location = (-120, -120)
-
-            noise.inputs["Scale"].default_value = 80.0
-            noise.inputs["Detail"].default_value = 1.0
-
-            ramp.color_ramp.elements[0].position = 0.49
-            ramp.color_ramp.elements[1].position = 0.51
-
-            mix.blend_type = "MIX"
-            mix.inputs["Fac"].default_value = aruco_dust_strength
-            mix.inputs["Color2"].default_value = (0.97, 0.97, 0.97, 1.0)
-            
-
-        if aruco_dust:
-            links.new(tex.outputs["Color"], mix.inputs["Color1"])
-            links.new(noise.outputs["Fac"], ramp.inputs["Fac"])
-            links.new(ramp.outputs["Color"], mix.inputs["Fac"])
-            links.new(mix.outputs["Color"], bsdf.inputs["Base Color"])
-        else:
-            links.new(tex.outputs["Color"], bsdf.inputs["Base Color"])        
-            
-        links.new(bsdf.outputs["BSDF"], out.inputs["Surface"])
-
-        return mat
-
-
-    def normal_to_quaternion(normal_vec):
-        normal = mathutils.Vector(normal_vec).normalized()
-
-        default_normal = mathutils.Vector((0, 0, 1))
-
-        return default_normal.rotation_difference(normal)
-
-
-    if show_markers:
-
-        marker_defaults = rendering_info.get("markerDefaults", {}) or {}
-
-        for m in link_info.get("markers", []):
-
-            if not isinstance(m, dict):
-                continue
-
-            marker_id = int(m.get("id", 0))
-
-            marker_name = m.get(
-                "name",
-                f"{link_name}_marker_{marker_id}"
-            )
-
-            marker_size_mm = float(
-                m.get(
-                    "size",
-                    marker_defaults.get("size", 25)
-                )
-            )
-
-            marker_pos = resolve_vec3_m(
-                m.get("position", [0, 0, 0]),
-                state
-            )
-
-            normal = m.get("normal", [0, 0, 1])
-            marker_spin_deg = float(m.get("spin", 0.0))
-
-            mesh = bpy.data.meshes.new(marker_name + "_mesh")
-
-            s = mm_to_m(marker_size_mm) * 0.5
-
-            verts = [
-                (-s,-s,0),
-                ( s,-s,0),
-                ( s, s,0),
-                (-s, s,0),
-            ]
-            faces = [(0,1,2,3)]
-
-            mesh.from_pydata(verts, [], faces)
-            mesh.update()
-
-            marker_obj = bpy.data.objects.new(marker_name,mesh)
-
-            bpy.context.collection.objects.link(marker_obj)
-            marker_obj.name = marker_name
-
-            safe_parent(marker_obj, link_frame, keep_world=False)
-
-            marker_obj.rotation_mode = "QUATERNION"
-            #alt: base_quat = normal_to_quaternion(normal)
-
-            normal = mathutils.Vector(m.get("normal", [0, 0, 1]))
-            if normal.length == 0:
-                normal = mathutils.Vector((0, 0, 1))
-            normal.normalize()
-
-            base_quat = normal_to_quaternion(normal)
-
-            spin_quat = mathutils.Quaternion(
-                mathutils.Vector((0, 0, 1)),
-                math.radians(marker_spin_deg)
-            )
-            
-            marker_obj.rotation_quaternion = (
-                base_quat @ spin_quat
-            )
-
-
-            # Marker-Normale im lokalen Link-Raum (aus Marker-Rotation)
-            normal_local = (
-                marker_obj.rotation_quaternion
-                @ mathutils.Vector((0, 0, 1))
-            )
-            normal_local.normalize()
-
-            # minimal vorziehen gegen Z-Fighting (lokaler Versatz)
-            marker_obj.location = (
-                mathutils.Vector(marker_pos)
-                + normal_local * mm_to_m(0.5)
-            )
-
-            marker_mat = create_aruco_material(
-                marker_id,
-                marker_name
-            )
-
-            if len(marker_obj.data.materials) == 0:
-                marker_obj.data.materials.append(marker_mat)
-            else:
-                marker_obj.data.materials[0] = marker_mat    
-
-            # --------------------------------------------------------
-            # --------------------------------------------------------
-            # BACKING PLATE (white PLA behind marker)
-            # --------------------------------------------------------
-
-            plate_side_mm = 26.5
-            plate_thickness_mm = 1.0
-            gap_mm = 0.2   # kleiner Abstand gegen Z-Fighting
-
-            mesh = bpy.data.meshes.new(
-                marker_name + "_plate_mesh"
-            )
-
-            verts = [
-            (-0.5,-0.5,-0.5),
-            ( 0.5,-0.5,-0.5),
-            ( 0.5, 0.5,-0.5),
-            (-0.5, 0.5,-0.5),
-            (-0.5,-0.5, 0.5),
-            ( 0.5,-0.5, 0.5),
-            ( 0.5, 0.5, 0.5),
-            (-0.5, 0.5, 0.5),
-            ]
-
-            faces = [
-            (0,1,2,3),
-            (4,5,6,7),
-            (0,1,5,4),
-            (1,2,6,5),
-            (2,3,7,6),
-            (3,0,4,7),
-            ]
-
-            mesh.from_pydata(
-                verts,
-                [],
-                faces
-            )
-
-            mesh.update()
-
-            plate_obj = bpy.data.objects.new(
-                marker_name + "_plate",
-                mesh
-            )
-
-            bpy.context.collection.objects.link(
-                plate_obj
-            )
-            plate_obj.name = marker_name + "_plate"
-
-            safe_parent(plate_obj, link_frame, keep_world=False)
-
-            # gleiche Orientierung wie der Marker
-            plate_obj.rotation_mode = "QUATERNION"
-            plate_obj.rotation_quaternion = marker_obj.rotation_quaternion.copy()
-
-            # Normale des Markers im lokalen Link-Raum (aus Marker-Rotation)
-            normal_local = marker_obj.rotation_quaternion @ mathutils.Vector((0, 0, 1))
-            normal_local.normalize()
-
-            # Platte liegt "hinter" dem Marker (lokaler Versatz)
-            plate_obj.location = (
-                marker_obj.location
-                - normal_local * mm_to_m((plate_thickness_mm * 0.5) + gap_mm)
-            )
-
-            # exakte Abmessungen: 26 x 26 x 1 mm
-            plate_obj.dimensions = (
-                mm_to_m(plate_side_mm),
-                mm_to_m(plate_side_mm),
-                mm_to_m(plate_thickness_mm)
-            )
-
-            pla_mat = create_or_get_material("plaWhite")
-            if len(plate_obj.data.materials) == 0:
-                plate_obj.data.materials.append(pla_mat)
-            else:
-                plate_obj.data.materials[0] = pla_mat
-            
-            # Weltmatrix holen (inkl. ALLER Transformationen!)
-            mw = marker_obj.matrix_world
-
-            world_pos = mw.translation
-            world_rot = mw.to_quaternion()
-
-            # Optional: Normal in Weltkoordinaten
-            world_normal = (world_rot @ mathutils.Vector((0, 0, 1))).normalized()
-
-            marker_export.append({
-                "name": marker_name,
-                "id": marker_id,
-                "link": link_name,
-
-                "position_m": [world_pos.x, world_pos.y, world_pos.z],
-
-                # oft nützlich für Computer Vision:
-                "position_mm": [
-                    world_pos.x / scale_factor,
-                    world_pos.y / scale_factor,
-                    world_pos.z / scale_factor
-                ],
-
-                "rotation_quaternion": [
-                    world_rot.w,
-                    world_rot.x,
-                    world_rot.y,
-                    world_rot.z
-                ],
-
-                "normal": [
-                    world_normal.x,
-                    world_normal.y,
-                    world_normal.z
-                ]
-            })
-
-            
-
-print("7 RENDER & EXPORT")
-# ============================================================
-# DEBUG WORLD AXES
-# ============================================================
-
-def create_axis_arrow(
-    name,
-    direction,
-    color,
-    length_mm=200,
-    radius_mm=2,
-    cone_radius_mm=5,
-    cone_length_mm=20
-):
-    length = mm_to_m(length_mm)
-    radius = mm_to_m(radius_mm)
-    cone_radius = mm_to_m(cone_radius_mm)
-    cone_length = mm_to_m(cone_length_mm)
-
-    dir_vec = mathutils.Vector(direction).normalized()
-
-    bpy.ops.mesh.primitive_cylinder_add(
-        radius=radius,
-        depth=length - cone_length
-    )
-
-    cyl = bpy.context.active_object
-    cyl.name = f"{name}_shaft"
-    cyl.rotation_mode = 'QUATERNION'
-    cyl.rotation_quaternion = mathutils.Vector((0, 0, 1)).rotation_difference(dir_vec)
-    cyl.location = dir_vec * ((length - cone_length) * 0.5)
-
-    bpy.ops.mesh.primitive_cone_add(
-        radius1=cone_radius,
-        depth=cone_length
-    )
-
-    cone = bpy.context.active_object
-    cone.name = f"{name}_tip"
-    cone.rotation_mode = 'QUATERNION'
-    cone.rotation_quaternion = mathutils.Vector((0, 0, 1)).rotation_difference(dir_vec)
-    cone.location = dir_vec * (length - cone_length * 0.5)
-
-    mat = bpy.data.materials.new(name=f"{name}_material")
-    mat.use_nodes = True
-    bsdf = mat.node_tree.nodes["Principled BSDF"]
-    bsdf.inputs["Base Color"].default_value = (color[0], color[1], color[2], 1.0)
-    bsdf.inputs["Roughness"].default_value = 0.3
-    bsdf.inputs["Metallic"].default_value = 0.0
-
-    cyl.data.materials.append(mat)
-    cone.data.materials.append(mat)
-
-create_axis_arrow("AxisX", (1, 0, 0), (1, 0, 0))
-create_axis_arrow("AxisY", (0, 1, 0), (0, 1, 0))
-create_axis_arrow("AxisZ", (0, 0, 1), (0, 0, 1))
-
-
-# ============================================================
-# RENDER
-# ============================================================
-
-bpy.ops.render.render(write_still=True)
-print("Finished rendering:", OUTPUT_FILE)
-
-
-
-MARKER_OUTPUT = str(Path(OUTPUT_FILE).with_name("markers.json"))
-
-with open(MARKER_OUTPUT, "w", encoding="utf-8") as f:
-    json.dump(marker_export, f, indent=2)
-
-print("Saved marker positions:", MARKER_OUTPUT)