Recently, several deep learning approaches have been proposed for point cloud registration. These methods train a network to generate a representation that helps finding matching points in two 3D point clouds. Finding good matches allows them to calculate the transformation between the point clouds accurately. Two challenges of these techniques are dealing with occlusions and generalizing to objects of classes unseen during training. This work proposes DeepBBS, a novel method for learning a representation that takes into account the best buddy distance between points during training. Best Buddies (i.e., mutual nearest neighbors) are pairs of points nearest to each other. The Best Buddies criterion is a strong indication for correct matches that, in turn, leads to accurate registration. Our experiments show improved performance compared to previous methods. In particular, our learned representation leads to an accurate registration for partial shapes and in unseen categories.

翻译：最近,为点云登记提出了几种深层次的学习方法。 这些方法对网络进行了培训, 以生成一个代表点, 帮助在两个 3D 点云中找到匹配点。 找到好匹配点可以让他们准确计算点云之间的转换。 这些技术的两大挑战涉及在培训期间对课堂对象的隔离和概括化。 这项工作提出了深BBS, 这是学习代表点的一种新颖方法, 考虑到了培训期间各点之间的最佳伙伴距离。 最佳伙伴( 即相邻邻居) 是彼此最接近的一对点。 最佳伙伴标准是准确匹配点的有力标志, 反过来又导致准确的登记。 我们的实验显示, 与以往方法相比, 成绩有所改善。 特别是, 我们的学习代表点导致对部分形状和不可见的类别进行准确的登记。