Multi-Confidence Guided Source-Free Domain Adaption Method for Point Cloud Primitive Segmentation

Point cloud primitive segmentation aims to segment the surface point cloud into various geometric types of primitives, which plays a vital role in robot operation and industrial automation. However, differences in object structures and shapes across industrial datasets create domain shift issues, compounded by privacy concerns preventing dataset sharing. To address these challenges, we propose a novel source-free domain adaptation method for point cloud primitive segmentation, which follows the popular pseudo-label based self-training framework. Unlike previous works using single-model uncertainty to refine pseudo labels, our method leverages multi-confidence, including transformation consistency, task confidence, and geometric saliency to provide more informative guidance. Specifically, the transformation consistency is first utilized to vote pseudo-labels and task confidences. Furthermore, to filter out high-confident noises and obtain more reliable pseudo-labels, we investigate the geometric curvature properties of primitives and propose a geometric saliency guided dynamic prototype matching and label graph aggregation strategies for pseudo-label reassignment with different task confidence. For this novel task, we construct several datasets and verify the effectiveness of the proposed methods through a series of experiments.