Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network

doi:10.1109/CVPR52733.2024.02096

	Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network
	Wenqiao Li1 ; Xiaohao Xu 2; Yao Gu1 ; Bozhong Zheng1 ; Shenghua Gaol1 ; Yingna Wu1
	2024-06-22
会议录名称	2024 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
ISSN	1063-6919
发表状态	已发表
DOI	10.1109/CVPR52733.2024.02096
摘要	Recently, 3D anomaly detection, a crucial problem in-volving fine-grained geometry discrimination, is getting more attention. However, the lack of abundant real 3D anomaly data limits the scalability of current models. To enable scalable anomaly data collection, we propose a 3D anomaly synthesis pipeline to adapt existing large-scale 3D models for 3D anomaly detection. Specifically, we construct a synthetic dataset, i.e., Anomaly-ShapeNet, based on ShapeNet. Anomaly-ShapeNet consists of 1600 point cloud samples under 40 categories, which provides a rich and varied collection of data, enabling efficient training and enhancing adaptability to industrial scenarios. Meanwhile, to enable scalable representation learning for 3D anomaly localization, we propose a self-supervised method, i.e., It-erative Mask Reconstruction Network (IMRNet). During training, we propose a geometry-aware sample module to preserve potentially anomalous local regions during point cloud down-sampling. Then, we randomly mask out point patches and sent the visible patches to a trans-former for reconstruction-based self-supervision. During testing, the point cloud repeatedly goes through the Mask Reconstruction Network, with each iteration's output be-coming the next input. By merging and contrasting the final reconstructed point cloud with the initial input, our method successfully locates anomalies. Experiments show that IMRNet outperforms previous state-of-the-art methods, achieving 66.1% in I-AUC on our Anomaly-ShapeNet dataset and 72.5% in I-AUC on ReaI3D-AD dataset. Our benchmark will be released at https://github.com/Chopper-233/Anomaly-ShapeNet.
会议地点	Seattle, WA, USA
会议日期	16-22 June 2024
URL	查看原文
来源库	IEEE
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/424437
专题	信息科学与技术学院_硕士生信息科学与技术学院_PI研究组_高盛华组创意与艺术学院_PI研究组(P)_武颖娜组
作者单位	1.ShanghaiTech University 2.University of Michigan, Ann Arbor
第一作者单位	上海科技大学
第一作者的第一单位	上海科技大学
推荐引用方式 GB/T 7714	Wenqiao Li,Xiaohao Xu,Yao Gu,et al. Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network[C],2024.