Shape assembly aims to reassemble parts (or fragments) into a complete
object, which is a common task in our daily life. Different from the semantic
part assembly (e.g., assembling a chair's semantic parts like legs into a whole
chair), geometric part assembly (e.g., assembling bowl fragments into a
complete bowl) is an emerging task in computer vision and robotics. Instead of
semantic information, this task focuses on geometric information of parts. As
the both geometric and pose space of fractured parts are exceptionally large,
shape pose disentanglement of part representations is beneficial to geometric
shape assembly. In our paper, we propose to leverage SE(3) equivariance for
such shape pose disentanglement. Moreover, while previous works in vision and
robotics only consider SE(3) equivariance for the representations of single
objects, we move a step forward and propose leveraging SE(3) equivariance for
representations considering multi-part correlations, which further boosts the
performance of the multi-part assembly. Experiments demonstrate the
significance of SE(3) equivariance and our proposed method for geometric shape
assembly. Project page: this https URL

我们提出利用 SE (3) 等变性对形状姿态进行解耦，并在多部分协作方面提供了改进，这大大提高了几何形状装配的性能。