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

• URL: http://arxiv.org/abs/2602.10806v1
• Date: Wed, 11 Feb 2026 12:47:38 GMT
• Title: DMP-3DAD: Cross-Category 3D Anomaly Detection via Realistic Depth Map Projection with Few Normal Samples
• Authors: Zi Wang, Katsuya Hotta, Koichiro Kamide, Yawen Zou, Jianjian Qin, Chao Zhang, Jun Yu,
• Abstract summary: Cross-category anomaly detection for 3D point clouds aims to determine whether an unseen object belongs to a target category.<n>Most existing methods rely on category-specific training, which limits their flexibility in few-shot scenarios.<n>DMP-3DAD is a training-free framework for cross-category 3D anomaly detection based on multi-view realistic depth map projection.
• Score: 15.21047221062711
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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