Sight View Constraint for Robust Point Cloud Registration
This work addresses robustness in point cloud registration for applications like robotics and 3D reconstruction, offering an incremental improvement over existing methods.
The paper tackles the challenge of partial-to-partial point cloud registration with low overlap by proposing a Sight View Constraint to identify incorrect transformations, improving registration recall from 78% to 82% on the 3DLoMatch dataset.
Partial to Partial Point Cloud Registration (partial PCR) remains a challenging task, particularly when dealing with a low overlap rate. In comparison to the full-to-full registration task, we find that the objective of partial PCR is still not well-defined, indicating no metric can reliably identify the true transformation. We identify this as the most fundamental challenge in partial PCR tasks. In this paper, instead of directly seeking the optimal transformation, we propose a novel and general Sight View Constraint (SVC) to conclusively identify incorrect transformations, thereby enhancing the robustness of existing PCR methods. Extensive experiments validate the effectiveness of SVC on both indoor and outdoor scenes. On the challenging 3DLoMatch dataset, our approach increases the registration recall from 78\% to 82\%, achieving the state-of-the-art result. This research also highlights the significance of the decision version problem of partial PCR, which has the potential to provide novel insights into the partial PCR problem.