{"type":"ScholarlyArticle","title":"MC3D-AD: A Unified Geometry-aware Reconstruction Model for Multi-category 3D Anomaly Detection","authors":[{"name":"Jiayi Cheng","slug":"jiayi-cheng","given":"Jiayi","family":"Cheng","affiliation":""},{"name":"Can Gao","slug":"can-gao","given":"Can","family":"Gao","affiliation":""},{"name":"Jie Zhou","slug":"jie-zhou","given":"Jie","family":"Zhou","affiliation":""},{"name":"Jiajun Wen","slug":"jiajun-wen","given":"Jiajun","family":"Wen","affiliation":""},{"name":"Tao Dai","slug":"tao-dai","given":"Tao","family":"Dai","affiliation":""},{"name":"Jinbao Wang","slug":"jinbao-wang","given":"Jinbao","family":"Wang","affiliation":""}],"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.","doi":"10.24963/ijcai.2025/94","url":"https://ijcai.org/proceedings/2025/94","pdf":"https://ijcai.org/proceedings/2025/0094.pdf","markdown":"https://ijcai.org/proceedings/2025/94.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/94","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Computer Vision","track":"Main Track","number":94,"pages":{"first":837,"last":845},"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"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}