DMP-3DAD: Cross-Category 3D Anomaly Detection via Realistic Depth Map Projection with Few Normal Samples

2026-02-11

AI Summary

This work addresses the challenge of cross-category 3D point cloud anomaly detection using only a few normal samples by proposing the first training-free, general-purpose framework. The method projects 3D point clouds into multi-view realistic depth maps and leverages a frozen CLIP vision encoder to extract features, enabling anomaly identification through weighted feature similarity without any category-specific fine-tuning or adaptation. Experimental results demonstrate that the approach achieves state-of-the-art performance under few-shot settings on the ShapeNetPart dataset, significantly enhancing the generality, practicality, and robustness of cross-category 3D anomaly detection.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Cross-category anomaly detection for 3D point clouds aims to determine whether an unseen object belongs to a target category using only a few normal examples. Most existing methods rely on category-specific training, which limits their flexibility in few-shot scenarios. In this paper, we propose DMP-3DAD, a training-free framework for cross-category 3D anomaly detection based on multi-view realistic depth map projection. Specifically, by converting point clouds into a fixed set of realistic depth images, our method leverages a frozen CLIP visual encoder to extract multi-view representations and performs anomaly detection via weighted feature similarity, which does not require any fine-tuning or category-dependent adaptation. Extensive experiments on the ShapeNetPart dataset demonstrate that DMP-3DAD achieves state-of-the-art performance under few-shot setting. The results show that the proposed approach provides a simple yet effective solution for practical cross-category 3D anomaly detection.

Problem

• 3D anomaly detection
• cross-category
• few-shot
• point clouds
• anomaly detection

Innovation

• cross-category anomaly detection
• 3D point clouds
• depth map projection
• training-free framework
• CLIP visual encoder