PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection

TL;DR

This paper introduces PO3AD, a novel method for 3D point cloud anomaly detection that focuses on learning point offsets and uses normal vector-guided augmentation, leading to improved detection performance.

Contribution

The paper proposes a new approach emphasizing point offset learning and a normal vector-guided augmentation technique for better anomaly detection in 3D point clouds.

Findings

Outperforms state-of-the-art methods on Anomaly-ShapeNet and Real3D-AD datasets.

Achieves 9.0% and 1.4% improvements in AUC-ROC metrics.

Effectively captures normal data features for anomaly detection.

Abstract

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.