---
title: "MC3D-AD: A Unified Geometry-aware Reconstruction Model for Multi-category 3D Anomaly Detection"
authors:
  - "Jiayi Cheng"
  - "Can Gao"
  - "Jie Zhou"
  - "Jiajun Wen"
  - "Tao Dai"
  - "Jinbao Wang"
doi: "10.24963/ijcai.2025/94"
url: "https://ijcai.org/proceedings/2025/94"
pdf: "https://ijcai.org/proceedings/2025/0094.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "837-845"
track: "Main Track"
keywords:
  - "Computer Vision: CV: 3D computer vision"
  - "Computer Vision: CV: Representation learning"
  - "Computer Vision: CV: Recognition (object detection, categorization)"
  - "Computer Vision: CV: Segmentation, grouping and shape analysis"
---

# MC3D-AD: A Unified Geometry-aware Reconstruction Model for Multi-category 3D Anomaly Detection

Jiayi Cheng, Can Gao, Jie Zhou, Jiajun Wen, Tao Dai, Jinbao Wang

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 837-845. DOI: [10.24963/ijcai.2025/94](https://doi.org/10.24963/ijcai.2025/94)

## Abstract

3D Anomaly Detection (AD) is a promising means of controlling the quality of manufactured products. However, existing methods typically require carefully training a task-specific model for each category independently, leading to high cost, low efficiency, and weak generalization. This study presents a novel unified model for Multi-Category 3D Anomaly Detection (MC3D-AD) that aims to utilize both local and global geometry-aware information to reconstruct normal representations of all categories. First, to learn robust and generalized features of different categories, we propose an adaptive geometry-aware masked attention module that extracts geometry variation information to guide mask attention. Then, we introduce a local geometry-aware encoder reinforced by the improved mask attention to encode group-level feature tokens. Finally, we design a global query decoder that utilizes point cloud position embeddings to improve the decoding process and reconstruction ability. This leads to local and global geometry-aware reconstructed feature tokens for the 3D AD task. MC3D-AD is evaluated on two publicly available Real3D-AD and Anomaly-ShapeNet datasets, and exhibits significant superiority over current state-of-the-art single-category methods, achieving 3.1% and 9.3% improvement in object-level AUROC over Real3D-AD and Anomaly-ShapeNet, respectively. The code is available at https://github.com/iCAN-SZU/MC3D-AD.