GMetric-3D: Geometric Metric Encoding Based on Topological Consistency for 3D Industrial Defect Detection

The 3D industrial surface defect detection is a challenging task due to the small scale, irregular shape, and lack of stable semantic patterns of the defects. However, the existing 3D defect detection methods mainly rely on semantic and appearance features and fail to effectively capture these subtle geometric variations. To address this issue, we propose the geometric metric encoding based on topological consistency (GMetric-3D) that explicitly models the local geometric structure in defects. Specifically, we first introduce the geometric metric encoding to capture local structural patterns by combining metrics such as relative positions, normal vector variations, and curvature information within the point neighborhood. To further enhance the distinguishability of defect features, we present the topological consistency learning, which retains topological structure in normal regions and expands feature differences in defect regions through topological smoothing, separation, and boundary perception constraints. Extensive experiments on the high-precision 3D point cloud dataset CPS3D-Det and the large-scale scene dataset KITTI demonstrate that our GMetric-3D achieves state-of-the-art performance compared with existing 3D detection methods. The experimental results confirm that modeling geometric structure information is crucial for reliable and accurate 3D industrial defect detection. The source code will be released.