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

DeepBBS: Deep Best Buddies for Point Cloud Registration