3D point cloud registration is a crucial task in a variety of fields, including remote sensing mapping, computer vision, virtual reality, and autonomous driving. However, this task is still challenging due to the challenges of noise, non-uniformity, partial overlap, and repeated local features in large scene point clouds. In this paper, we propose an efficient single correspondence voting method for large scene point cloud registration. Specifically, we first propose an efficient hypothetical transformation prediction method called SCVC, which determines the 5 degrees of freedom of the transformation through one correspondence, and then uses Hough voting to determine the last degree of freedom. This algorithm can significantly improve the accuracy of registration in both indoor and outdoor scenes. On the other hand, we propose a more robust transformation verification function called VDIR, which can obtain the optimal registration result of two raw point clouds. Finally, we conduct a series of experiments that demonstrate that our method achieves state-of-the-art performance on four real-world datasets: 3DMatch, 3DLoMatch, KITTI, and WHU-TLS. Our code is available at https://github.com/xingxuejun1989/SCVC.

We present a novel and efficient approach to estimate 6D object poses of known objects in complex scenes represented by point clouds. Our approach is based on the well-known point pair feature (PPF) matching, which utilizes self-similar point pairs to compute potential matches and thereby cast votes for the object pose by a voting scheme. The main contribution of this paper is to present an improved PPF-based recognition framework, especially a new center voting strategy based on the relative geometric relationship between the object center and point pair features. Using this geometric relationship, we first generate votes to object centers resulting in vote clusters near real object centers. Then we group and aggregate these votes to generate a set of pose hypotheses. Finally, a pose verification operator is performed to filter out false positives and predict appropriate 6D poses of the target object. Our approach is also suitable to solve the multi-instance and multi-object detection tasks. Extensive experiments on a variety of challenging benchmark datasets demonstrate that the proposed algorithm is discriminative and robust towards similar-looking distractors, sensor noise, and geometrically simple shapes. The advantage of our work is further verified by comparing to the state-of-the-art approaches.