M2P-AD: Memory-to-Prototype Learning with Boundary-aware Score Refinement for 3D Anomaly Detection

2026-07-15

AI Summary

This work addresses the challenges of over-response in normal regions and false positives near object boundaries in 3D anomaly detection by proposing a novel approach that integrates a Memory-to-Prototype (M2P) module with a Boundary-aware Score Refinement (BSR) strategy. The M2P module leverages a memory bank to learn representative prototypes of normal features, enabling precise modeling of the normal distribution. Concurrently, a boundary extraction module is introduced to facilitate structure-aware correction of anomaly scores through BSR, effectively preserving geometric integrity while suppressing boundary artifacts. Evaluated on Real3D-AD, Anomaly-ShapeNet, and MulSen-AD datasets, the proposed method significantly reduces false alarms in normal regions and boundary-related false positives, achieving more accurate and robust anomaly localization and outperforming current state-of-the-art methods.

Abstract

3D anomaly detection has recently emerged as an important research topic in computer vision. Although existing methods have achieved high performance, excessive anomaly responses in normal regions and false positives near object boundaries remain unresolved challenges. To address these challenges, we propose a novel 3D anomaly detection model, Memory-to-Prototype Anomaly Detection (M2P-AD), which effectively models the distribution of normal features while suppressing excessive anomaly scores in normal regions and false positives near object boundaries. Specifically, we introduce a Memory-to-Prototype (M2P) module that learns representative prototypes from normal feature embeddings to preserve important structural information of objects. In addition, a Boundary extraction (BE) module is integrated to identify object boundaries, and a Boundary-aware score refinement (BSR) strategy is applied to recalibrate anomaly scores by incorporating boundary characteristics. The proposed method is evaluated on Real3D-AD, Anomaly-ShapeNet, and MulSen-AD, achieving state-of-the-art performance. Qualitative results demonstrate that excessive anomaly scores in normal regions are reduced and false positives near object boundaries are suppressed, resulting in more accurate and stable anomaly localization. The results indicate that the proposed approach enables more reliable 3D anomaly detection and provides a robust solution applicable to real-world industrial environments.

Problem

• 3D anomaly detection
• false positives
• object boundaries
• anomaly responses
• normal regions

Innovation

• Memory-to-Prototype Learning
• Boundary-aware Score Refinement
• 3D Anomaly Detection
• Prototype Representation
• False Positive Suppression