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Models/M3DM

M3DM

Reported on 7 benchmarks across 2 tasks · 1 paper · 1 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Methodology5 results

Anomaly DetectiononMVTEC 3D-AD
Detection AUCROC· uses extra data· 2023-03-01
0.945
best: 0.982 (TransFusion)
SOTA
Multimodal Industrial Anomaly Detection via Hybrid Fusion arXiv:2303.00601
Anomaly DetectiononAnomaly-ShapeNet10
O-AUROC· 2023-03-01
0.574
best: 0.888 (MC4AD)
Multimodal Industrial Anomaly Detection via Hybrid Fusion arXiv:2303.00601
Anomaly DetectiononAnomaly-ShapeNet10
P-AUROC· 2023-03-01
0.648
best: 0.937 (MC4AD)
Multimodal Industrial Anomaly Detection via Hybrid Fusion arXiv:2303.00601
Anomaly DetectiononMVTEC 3D-AD
Segmentation AUCROC· uses extra data· 2023-03-01
0.992
best: 0.996 (Shape-Guided)
Multimodal Industrial Anomaly Detection via Hybrid Fusion arXiv:2303.00601
Anomaly DetectiononMVTEC 3D-AD
Segmentation AUPRO· uses extra data· 2023-03-01
0.964
best: 93.75 (CDO)
Multimodal Industrial Anomaly Detection via Hybrid Fusion arXiv:2303.00601

Computer Vision2 results

3D Anomaly DetectiononAnomaly-ShapeNet10
O-AUROC· 2023-03-01
0.574
best: 0.888 (MC4AD)
Multimodal Industrial Anomaly Detection via Hybrid Fusion arXiv:2303.00601
3D Anomaly DetectiononAnomaly-ShapeNet10
P-AUROC· 2023-03-01
0.648
best: 0.937 (MC4AD)
Multimodal Industrial Anomaly Detection via Hybrid Fusion arXiv:2303.00601