The core message of this paper is that the sight view constraint (SVC) can effectively identify incorrect transformations in partial point cloud registration tasks, thereby enhancing the robustness of existing registration methods, especially for low-overlap scenarios.
The authors propose a heuristics-guided parameter search strategy that enjoys an excellent trade-off between efficiency and robustness for point cloud registration. The method decomposes the original 6-DoF registration problem into three lower-dimensional sub-problems and applies the heuristics-guided parameter search in the solving progress to perform progressive outlier removal.