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Models/PointAD

PointAD

Reported on 6 benchmarks across 2 tasks · 1 paper · 2 SOTA

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
Object AUROC· 2024-10-01
0.759
best: 0.802 (PASDF)
SOTA
PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detection arXiv:2410.00320
Anomaly DetectiononReal 3D-AD
Mean Performance of P. and O. · 2024-10-01
0.7375
best: 0.821 (DUS-Net)
PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detection arXiv:2410.00320
Anomaly DetectiononReal 3D-AD
Point AUROC· 2024-10-01
0.716
best: 0.898 (GLFM)
PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detection arXiv:2410.00320

Computer Vision3 results

3D Anomaly DetectiononReal 3D-AD
Object AUROC· 2024-10-01
0.759
best: 0.802 (PASDF)
SOTA
PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detection arXiv:2410.00320
3D Anomaly DetectiononReal 3D-AD
Mean Performance of P. and O. · 2024-10-01
0.7375
best: 0.821 (DUS-Net)
PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detection arXiv:2410.00320
3D Anomaly DetectiononReal 3D-AD
Point AUROC· 2024-10-01
0.716
best: 0.898 (GLFM)
PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detection arXiv:2410.00320