Papers › Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection
Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection
Eliahu Horwitz, Yedid Hoshen
Despite significant advances in image anomaly detection and segmentation, few methods use 3D information. We utilize a recently introduced 3D anomaly detection dataset to evaluate whether or not using 3D information is a lost opportunity. First, we present a surprising finding: standard color-only methods outperform all current methods that are explicitly designed to exploit 3D information. This is counter-intuitive as even a simple inspection of the dataset shows that color-only methods are insufficient for images containing geometric anomalies. This motivates the question: how can anomaly detection methods effectively use 3D information? We investigate a range of shape representations including hand-crafted and deep-learning-based; we demonstrate that rotation invariance plays the leading role in the performance. We uncover a simple 3D-only method that beats all recent approaches while not using deep learning, external pre-training datasets, or color information. As the 3D-only method cannot detect color and texture anomalies, we combine it with color-based features, significantly outperforming previous state-of-the-art. Our method, dubbed BTF (Back to the Feature) achieves pixel-wise ROCAUC: 99.3% and PRO: 96.4% on MVTec 3D-AD.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Anomaly Detection | Anomaly-ShapeNet10 | BTF (FPFH) | O-AUROC | 0.632 | #3 of 7 | Archive leaderboard | report |
| 3D Anomaly Detection | Anomaly-ShapeNet10 | BTF (FPFH) | P-AUROC | 0.790 | #3 of 7 | Archive leaderboard | report |
| 3D Anomaly Detection | Anomaly-ShapeNet10 | BTF (Raw) | O-AUROC | 0.500 | #7 of 7 | Archive leaderboard | report |
| 3D Anomaly Detection | Anomaly-ShapeNet10 | BTF (Raw) | P-AUROC | 0.515 | #7 of 7 | Archive leaderboard | report |
| 3D Anomaly Detection | Real 3D-AD | BTF (Raw) | Mean Performance of P. and O. | 0.6785 | #13 of 19 | Archive leaderboard | report |
| 3D Anomaly Detection | Real 3D-AD | BTF (Raw) | Object AUROC | 0.635 | #13 of 19 | Archive leaderboard | report |
| 3D Anomaly Detection | Real 3D-AD | BTF (Raw) | Point AUROC | 0.722 | #13 of 19 | Archive leaderboard | report |
| 3D Anomaly Detection | Real 3D-AD | BTF (FPFH) | Mean Performance of P. and O. | 0.5845 | #19 of 19 | Archive leaderboard | report |
| 3D Anomaly Detection | Real 3D-AD | BTF (FPFH) | Object AUROC | 0.603 | #19 of 19 | Archive leaderboard | report |
| 3D Anomaly Detection | Real 3D-AD | BTF (FPFH) | Point AUROC | 0.566 | #19 of 19 | Archive leaderboard | report |
| 3D Anomaly Detection and Segmentation | MVTEC 3D-AD | Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection (FPFH) | Detection AUROC | 0.782 | #7 of 11 | Archive leaderboard | report |
| 3D Anomaly Detection and Segmentation | MVTEC 3D-AD | Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection (FPFH) | Segmentation AUROC | 0.978 | #7 of 11 | Archive leaderboard | report |
| Depth Anomaly Detection and Segmentation | MVTEC 3D-AD | Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection (SIFT) | Detection AUROC | 0.727 | #5 of 13 | Archive leaderboard | report |
| Depth Anomaly Detection and Segmentation | MVTEC 3D-AD | Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection (SIFT) | Segmentation AUPRO | 0.910 | #5 of 13 | Archive leaderboard | report |
| Depth Anomaly Detection and Segmentation | MVTEC 3D-AD | Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection (SIFT) | Segmentation AUROC | 0.974 | #5 of 13 | Archive leaderboard | report |
| Depth Anomaly Detection and Segmentation | MVTEC 3D-AD | Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection (HoG) | Detection AUROC | 0.559 | #7 of 13 | Archive leaderboard | report |
| Depth Anomaly Detection and Segmentation | MVTEC 3D-AD | Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection (HoG) | Segmentation AUPRO | 0.771 | #7 of 13 | Archive leaderboard | report |
| Depth Anomaly Detection and Segmentation | MVTEC 3D-AD | Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection (HoG) | Segmentation AUROC | 0.930 | #7 of 13 | Archive leaderboard | report |
| Depth Anomaly Detection and Segmentation | MVTEC 3D-AD | Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection (Depth iNet) | Detection AUROC | 0.675 | #8 of 13 | Archive leaderboard | report |
| Depth Anomaly Detection and Segmentation | MVTEC 3D-AD | Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection (Depth iNet) | Segmentation AUPRO | 0.755 | #8 of 13 | Archive leaderboard | report |
| Depth Anomaly Detection and Segmentation | MVTEC 3D-AD | Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection (Depth iNet) | Segmentation AUROC | 0.930 | #8 of 13 | Archive leaderboard | report |
| Depth Anomaly Detection and Segmentation | MVTEC 3D-AD | Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection (NSA) | Detection AUROC | 0.696 | #9 of 13 | Archive leaderboard | report |
| Depth Anomaly Detection and Segmentation | MVTEC 3D-AD | Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection (NSA) | Segmentation AUPRO | 0.5572 | #9 of 13 | Archive leaderboard | report |
| Depth Anomaly Detection and Segmentation | MVTEC 3D-AD | Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection (NSA) | Segmentation AUROC | 0.817 | #9 of 13 | Archive leaderboard | report |
| Depth Anomaly Detection and Segmentation | MVTEC 3D-AD | Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection (RaW) | Detection AUROC | 0.573 | #10 of 13 | Archive leaderboard | report |
| Depth Anomaly Detection and Segmentation | MVTEC 3D-AD | Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection (RaW) | Segmentation AUPRO | 0.442 | #10 of 13 | Archive leaderboard | report |
| Depth Anomaly Detection and Segmentation | MVTEC 3D-AD | Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection (RaW) | Segmentation AUROC | 0.771 | #10 of 13 | Archive leaderboard | report |
| RGB+3D Anomaly Detection and Segmentation | MVTEC 3D-AD | Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection (BTF) | Detection AUCROC | 0.865 | #6 of 9 | Archive leaderboard | report |
| RGB+3D Anomaly Detection and Segmentation | MVTEC 3D-AD | Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection (BTF) | Segmentation AUCROC | 0.992 | #6 of 9 | Archive leaderboard | report |
| RGB+3D Anomaly Detection and Segmentation | MVTEC 3D-AD | Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection (BTF) | Segmentation AUPRO | 0.959 | #6 of 9 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: BTF
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