Xing Xuejun, Lu Zhengda, Wang Yiqun, Xiao Jun
IEEE Trans Image Process. 2024;33:2116-2130. doi: 10.1109/TIP.2024.3374120. Epub 2024 Mar 18.
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.
三维点云配准是包括遥感测绘、计算机视觉、虚拟现实和自动驾驶在内的多个领域中的一项关键任务。然而，由于大场景点云中存在噪声、不均匀性、部分重叠以及重复局部特征等挑战，该任务仍然具有挑战性。在本文中，我们提出了一种用于大场景点云配准的高效单对应投票方法。具体而言，我们首先提出了一种名为SCVC的高效假设变换预测方法，该方法通过一次对应关系确定变换的5个自由度，然后使用霍夫投票确定最后一个自由度。该算法能够显著提高室内和室外场景中的配准精度。另一方面，我们提出了一种更稳健的变换验证函数VDIR，它可以获得两个原始点云的最优配准结果。最后，我们进行了一系列实验，结果表明我们的方法在四个真实世界数据集（3DMatch、3DLoMatch、KITTI和WHU-TLS）上达到了当前最优性能。我们的代码可在https://github.com/xingxuejun1989/SCVC获取。
