Papers › PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection
PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection
Jianan Ye, Weiguang Zhao, Xi Yang, Guangliang Cheng, Kaizhu Huang
Point cloud anomaly detection under the anomaly-free setting poses significant challenges as it requires accurately capturing the features of 3D normal data to identify deviations indicative of anomalies. Current efforts focus on devising reconstruction tasks, such as acquiring normal data representations by restoring normal samples from altered, pseudo-anomalous counterparts. Our findings reveal that distributing attention equally across normal and pseudo-anomalous data tends to dilute the model's focus on anomalous deviations. The challenge is further compounded by the inherently disordered and sparse nature of 3D point cloud data. In response to those predicaments, we introduce an innovative approach that emphasizes learning point offsets, targeting more informative pseudo-abnormal points, thus fostering more effective distillation of normal data representations. We also have crafted an augmentation technique that is steered by normal vectors, facilitating the creation of credible pseudo anomalies that enhance the efficiency of the training process. Our comprehensive experimental evaluation on the Anomaly-ShapeNet and Real3D-AD datasets evidences that our proposed method outperforms existing state-of-the-art approaches, achieving an average enhancement of 9.0% and 1.4% in the AUC-ROC detection metric across these datasets, respectively.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
No code repository is listed for this paper in the archive or in Syntology's graph.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Anomaly Detection | Anomaly-ShapeNet | PO3AD | O-AUROC | 0.839 | #4 of 8 | Archive leaderboard | report |
| 3D Anomaly Detection | Anomaly-ShapeNet | PO3AD | P-AUROC | 0.898 | #4 of 8 | Archive leaderboard | report |
| 3D Anomaly Detection | Real 3D-AD | PO3AD | Mean Performance of P. and O. | 0.765 | #6 of 19 | Archive leaderboard | report |
| 3D Anomaly Detection | Real 3D-AD | PO3AD | Object AUROC | 0.765 | #6 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.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections