Papers › Taming Anomalies with Down-Up Sampling Networks: Group Center Preserving...
Taming Anomalies with Down-Up Sampling Networks: Group Center Preserving Reconstruction for 3D Anomaly Detection
Hanzhe Liang, Jie Zhang, Tao Dai, Linlin Shen, Jinbao Wang, Can Gao
Reconstruction-based methods have demonstrated very promising results for 3D anomaly detection. However, these methods face great challenges in handling high-precision point clouds due to the large scale and complex structure. In this study, a Down-Up Sampling Network (DUS-Net) is proposed to reconstruct high-precision point clouds for 3D anomaly detection by preserving the group center geometric structure. The DUS-Net first introduces a Noise Generation module to generate noisy patches, which facilitates the diversity of training data and strengthens the feature representation for reconstruction. Then, a Down-sampling Network~(Down-Net) is developed to learn an anomaly-free center point cloud from patches with noise injection. Subsequently, an Up-sampling Network (Up-Net) is designed to reconstruct high-precision point clouds by fusing multi-scale up-sampling features. Our method leverages group centers for construction, enabling the preservation of geometric structure and providing a more precise point cloud. Extensive experiments demonstrate the effectiveness of our proposed method, achieving state-of-the-art (SOTA) performance with an Object-level AUROC of 79.9% and 79.5%, and a Point-level AUROC of 71.2% and 84.7% on the Real3D-AD and Anomaly-ShapeNet datasets, respectively.
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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-ShapeNet | DUS-Net | O-AUROC | 0.797 | #5 of 8 | Archive leaderboard | report |
| 3D Anomaly Detection | Anomaly-ShapeNet | DUS-Net | P-AUROC | 0.712 | #5 of 8 | Archive leaderboard | report |
| 3D Anomaly Detection | Real 3D-AD | DUS-Net | Mean Performance of P. and O. | 0.821 | #1 of 19 | Archive leaderboard | report |
| 3D Anomaly Detection | Real 3D-AD | DUS-Net | Object AUROC | 0.795 | #1 of 19 | Archive leaderboard | report |
| 3D Anomaly Detection | Real 3D-AD | DUS-Net | Point AUROC | 0.847 | #1 of 19 | 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.
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