  Self-supervised pre-training for 3D vision has drawn increasing research
interest in recent years. In order to learn informative representations, a lot
of previous works exploit invariances of 3D features, e.g.,
perspective-invariance between views of the same scene, modality-invariance
between depth and RGB images, format-invariance between point clouds and
voxels. Although they have achieved promising results, previous researches lack
a systematic and fair comparison of these invariances. To address this issue,
our work, for the first time, introduces a unified framework, under which
various pre-training methods can be investigated. We conduct extensive
experiments and provide a closer look at the contributions of different
invariances in 3D pre-training. Also, we propose a simple but effective method
that jointly pre-trains a 3D encoder and a depth map encoder using contrastive
learning. Models pre-trained with our method gain significant performance boost
in downstream tasks. For instance, a pre-trained VoteNet outperforms previous
methods on SUN RGB-D and ScanNet object detection benchmarks with a clear
margin.
