Critical to the registration of point clouds is the establishment of a set of accurate correspondences between points in 3D space. The correspondence problem is generally addressed by the design of discriminative 3D local descriptors on the one hand, and the development of robust matching strategies on the other hand. In this work, we first propose a multi-view local descriptor, which is learned from the images of multiple views, for the description of 3D keypoints. Then, we develop a robust matching approach, aiming at rejecting outlier matches based on the efficient inference via belief propagation on the defined graphical model. We have demonstrated the boost of our approaches to registration on the public scanning and multi-view stereo datasets. The superior performance has been verified by the intensive comparisons against a variety of descriptors and matching methods.

翻译：点云配准的关键在于建立三维空间中点之间的精确对应关系。对应问题通常通过两方面解决：一是设计具有判别力的三维局部描述符，二是开发稳健的匹配策略。本文首先提出一种从多视角图像中学习得到的多视角局部描述符，用于描述三维关键点；其次开发了一种稳健的匹配方法，旨在通过在定义的图模型上利用信念传播进行高效推理来拒绝外点匹配。我们在公开扫描数据和多视角立体数据集上验证了所提方法对配准性能的提升。通过与多种描述符及匹配方法的密集比较，进一步证实了其优越性能。