A Closer Look at Invariances in Self-supervised Pre-training for 3D Vision
This work addresses the problem of evaluating invariances in 3D pre-training for researchers, but it is incremental as it builds on existing methods with a new comparison framework.
The paper tackles the lack of systematic comparison of invariances in self-supervised pre-training for 3D vision by introducing a unified framework and conducting extensive experiments, resulting in a method that boosts performance in downstream tasks, such as VoteNet outperforming previous methods on SUN RGB-D and ScanNet object detection benchmarks.
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.