+Figure 1: Segmentation mask distribution per anomaly detection class. +
+Figure 2: Image distribution per scene and anomaly detection task. +
+Figure 3: Binary Anomaly Classes & Distribution. +
| Property | +Value | +
|---|---|
| Total Images | 2,442 |
| Training Images (Normal Only) | 1,003 |
| Test Images | 1,439 |
| Number of Container Types | 3 |
| Modalities | RGB + Depth (RGB-D) |
| Cameras | Zivid 2+ M60, Intel RealSense D435 |
| Annotation Types | Segmentation Masks, 6D Pose, Image-Level Labels |
| Tasks | Emptiness Detection, Cleanliness Detection |
| CAD Models Included | Yes |
| Sim-to-Real Support | Yes |
| Label | +Description | +
|---|---|
| empty_clean | +Empty container with no contamination or residual content | +
| empty_dirtyy | +Empty container containing one or more nuisance contaminants | +
| non_empty | +Container containing residual objects, regardless of additional contaminants | +
|
+ +Figure 1: Segmentation mask distribution per anomaly detection class. + |
+
+
+ +Figure 2: Image distribution per scene and anomaly detection task. + |
+
+
+ +Figure 3: Binary Anomaly Classes & Distribution. + |
+
+ +Figure 1: mask_all. + |
+
+
+ +Figure 2: mask_nuisance + |
+
+
+ +Figure 3: mask_visib (box) + + |
+ +Figure 3: rgb (Anomalous) + |
+
+ +Figure 1: mask_visib (box). + |
+
+
+ +Figure 2: rgb (anomaly-free). + |
+