Progress in 3D object understanding has relied on manually canonicalized
shape datasets that contain instances with consistent position and orientation
(3D pose). This has made it hard to generalize these methods to in-the-wild
shapes, eg., from internet model collections or depth sensors. ConDor is a
self-supervised method that learns to Canonicalize the 3D orientation and
position for full and partial 3D point clouds. We build on top of Tensor Field
Networks (TFNs), a class of permutation- and rotation-equivariant, and
translation-invariant 3D networks. During inference, our method takes an unseen
full or partial 3D point cloud at an arbitrary pose and outputs an equivariant
canonical pose. During training, this network uses self-supervision losses to
learn the canonical pose from an un-canonicalized collection of full and
partial 3D point clouds. ConDor can also learn to consistently co-segment
object parts without any supervision. Extensive quantitative results on four
new metrics show that our approach outperforms existing methods while enabling
new applications such as operation on depth images and annotation transfer.

ConDor 是一种自监督学习方法，用于学习规范化 3D 位姿，能够处理来自互联网模型集合或深度传感器的任意姿态的物体，基于 Tensor Field Networks，利用自监督损失从非规范化的 3D 点云集合中学习规范化的 3D 位姿和共同切割物体部分，实现出色的定量结果和注释迁移等新应用。