M3DM (PointMAE)

Reported on 6 benchmarks across 2 tasks · 1 paper

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

Methodology3 results

Anomaly DetectiononReal 3D-AD
Mean Performance of P. and O. · 2023-03-01
0.5945
best: 0.821 (DUS-Net)
Multimodal Industrial Anomaly Detection via Hybrid Fusion arXiv:2303.00601
Anomaly DetectiononReal 3D-AD
Object AUROC· 2023-03-01
0.552
best: 0.802 (PASDF)
Multimodal Industrial Anomaly Detection via Hybrid Fusion arXiv:2303.00601
Anomaly DetectiononReal 3D-AD
Point AUROC· 2023-03-01
0.637
best: 0.898 (GLFM)
Multimodal Industrial Anomaly Detection via Hybrid Fusion arXiv:2303.00601

Computer Vision3 results

3D Anomaly DetectiononReal 3D-AD
Mean Performance of P. and O. · 2023-03-01
0.5945
best: 0.821 (DUS-Net)
Multimodal Industrial Anomaly Detection via Hybrid Fusion arXiv:2303.00601
3D Anomaly DetectiononReal 3D-AD
Object AUROC· 2023-03-01
0.552
best: 0.802 (PASDF)
Multimodal Industrial Anomaly Detection via Hybrid Fusion arXiv:2303.00601
3D Anomaly DetectiononReal 3D-AD
Point AUROC· 2023-03-01
0.637
best: 0.898 (GLFM)
Multimodal Industrial Anomaly Detection via Hybrid Fusion arXiv:2303.00601