In this paper, we propose a novel local descriptor-based framework, called You Only Hypothesize Once (YOHO), for the registration of two unaligned point clouds. In contrast to most existing local descriptors which rely on a fragile local reference frame to gain rotation invariance, the proposed descriptor achieves the rotation invariance by recent technologies of group equivariant feature learning, which brings more robustness to point density and noise. Meanwhile, the descriptor in YOHO also has a rotation equivariant part, which enables us to estimate the registration from just one correspondence hypothesis. Such property reduces the searching space for feasible transformations, thus greatly improves both the accuracy and the efficiency of YOHO. Extensive experiments show that YOHO achieves superior performances with much fewer needed RANSAC iterations on four widely-used datasets, the 3DMatch/3DLoMatch datasets, the ETH dataset and the WHU-TLS dataset. More details are shown in our project page: https://hpwang-whu.github.io/YOHO/.

翻译：在本文中,我们提出一个新的本地描述性框架,名为“你只假设一次”(YOHO),用于登记两个不匹配点云层。与大多数现有的本地描述性文件相比,大多数现有的描述性文件依靠脆弱的本地参考框架来获得轮换,而拟议的描述性文件则通过最近采用的群分特征学习技术实现交替变化,使点密度和噪音更加稳健。同时,YOHO的描述性文件也有一个旋转式的变异部分,使我们能够从一个对应假设中估计登记情况。这些属性减少了可行变换的搜索空间,从而极大地提高了YOHO的准确性和效率。广泛的实验表明,YOHO在四种广泛使用的数据集、3DMatch/3D LoMatch数据集、ET数据集和WHU-TLS数据集上实现了更优的高级性性能,而需要的RANSAC的反复作用要少得多。更多详情见我们的项目网页:https://hpwang-whu.github.io/YOHO/。