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In this paper, we propose a novel local descriptor-based framework, calledYou Only Hypothesize Once (YOHO), for the registration of two unaligned pointclouds. In contrast to most existing local descriptors which rely on a fragilelocal reference frame to gain rotation invariance, the proposed descriptorachieves the rotation invariance by recent technologies of group equivariantfeature learning, which brings more robustness to point density and noise.Meanwhile, the descriptor in YOHO also has a rotation equivariant part, whichenables us to estimate the registration from just one correspondencehypothesis. Such property reduces the searching space for feasibletransformations, thus greatly improves both the accuracy and the efficiency ofYOHO. Extensive experiments show that YOHO achieves superior performances withmuch fewer needed RANSAC iterations on four widely-used datasets, the3DMatch/3DLoMatch datasets, the ETH dataset and the WHU-TLS dataset. Moredetails are shown in our project page: https://hpwang-whu.github.io/YOHO/.

You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors