Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation

3D point cloud anomaly detection is essential for robust vision systems but is challenged by pose variations and complex geometric anomalies. Existing patch-based methods often suffer from geometric fidelity issues due to discrete voxelization or projection-based representations, limiting fine-grained anomaly localization. We introduce Pose-Aware Signed Distance Field (PASDF), a novel framework that integrates 3D anomaly detection and repair by learning a continuous, pose-invariant shape representation. PASDF leverages a Pose Alignment Module for canonicalization and a SDF Network to dynamically incorporate pose, enabling implicit learning of high-fidelity anomaly repair templates from the continuous SDF. This facilitates precise pixel-level anomaly localization through an Anomaly-Aware Scoring Module. Crucially, the continuous 3D representation in PASDF extends beyond detection, facilitating in-situ anomaly repair. Experiments on Real3D-AD and Anomaly-ShapeNet demonstrate state-of-the-art performance, achieving high object-level AUROC scores of 80.2% and 90.0%, respectively. These results highlight the effectiveness of continuous geometric representations in advancing 3D anomaly detection and facilitating practical anomaly region repair. The code is available atthis https URLto support further research.

@article{zheng2025_2505.24431,
  title={ Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation },
  author={ Bozhong Zheng and Jinye Gan and Xiaohao Xu and Wenqiao Li and Xiaonan Huang and Na Ni and Yingna Wu },
  journal={arXiv preprint arXiv:2505.24431},
  year={ 2025 }
}