Abstract
Point cloud registration (PCR) has been an important research subject for many years but remains an open problem, presenting numerous challenges. The stability of existing registration methods is often inadequate, particularly in scenarios with low overlap. This issue primarily arises from the insufficient distinctiveness of extracted point cloud features, leading to ambiguous matches and the proliferation of outliers. To address these bottlenecks in point cloud registration, it is crucial to fully leverage the color information of the point clouds to discern point correspondences effectively. However, excessive reliance on color information during the registration process may impede the recognition of point cloud geometric structures, making it essential to find a balance between the aggressiveness and stability of color integration. To tackle these challenges, we propose ColorPCR++, which unlocks the potential of color information while maintaining stability. Specifically, we design a Curvature-Color Fusion Module (CCF) to initially introduce color, which leverages curvature to enhance geometric features and balance color stability. Additionally, to further incorporate color into scene structure representation, we model the point-wise hue gradient field (HGF) for positional embedding, which proves especially effective in low-overlap scenarios. CCF and HGF fully harness the power of color, enabling the identification of most corresponding point pairs with prominent geometric and color features. However, the reliance on color can also introduce false correspondences, reducing the inlier ratio within the correspondences obtained through feature matching. Therefore, we assess the higher-order compatibility of correspondences using Feature-based Compatibility Hypergraph Convolution (FCH), effectively filtering out outliers and accurately estimating the transformation. Evaluation across multiple datasets has demonstrated the state-of-the-art performance of ColorPCR++.