We introduce Equivariant Neural Field Expectation Maximization (EFEM), a
simple, effective, and robust geometric algorithm that can segment objects in
3D scenes without annotations or training on scenes. We achieve such
unsupervised segmentation by exploiting single object shape priors. We make two
novel steps in that direction. First, we introduce equivariant shape
representations to this problem to eliminate the complexity induced by the
variation in object configuration. Second, we propose a novel EM algorithm that
can iteratively refine segmentation masks using the equivariant shape prior. We
collect a novel real dataset Chairs and Mugs that contains various object
configurations and novel scenes in order to verify the effectiveness and
robustness of our method. Experimental results demonstrate that our method
achieves consistent and robust performance across different scenes where the
(weakly) supervised methods may fail. Code and data available at
this https URL

本研究提出一种无需注释或依赖场景训练，利用深度学习进行三维物体分割的算法，通过利用单个物体形状先验信息，采用等变形状表示及新型 EM 算法进行无监督的物体分割。实验结果表明，本算法在有不同物体姿态的场景中表现一致且鲁棒性良好。