Partial-to-Partial Point Cloud Registration Based on Multi-Level Semantic-Structural Cognition

Abstract

3D point cloud registration attempt to establish spatial correspondences between the source point cloud and the target point cloud. It is a fundamental task in computer vision and multimedia applications. Recently, many learning-based methods have been proposed and achieved promising performance. However, in the Partial-to-Partial (PtP) registration problem, the existence of a large number of external points may greatly handicap the effectiveness of these methods. In this paper, we propose to address the PtP issue under a novel multi-task cognition framework. At the global semantic level, we introduce a multi-scale feature exchanging network to actively evaluate the matching credibility. For the local structural learning, an inner-inter attention fusion branch is applied to generate discriminative features. Moreover, we integrate a novel alternating correspondences searching mechanism with a flexible bi-directional dislocation loss to perform robust learning, and a simple yet effective SVD weighting scheme is introduced at the inference stage. Experiment results on two challenging PtP 3D point cloud registration data sets show that our proposed method outperforms all the SOTA methods with higher precision and robustness.