  We propose a novel approach to self-supervised learning of point cloud
representations by differentiable neural rendering. Motivated by the fact that
informative point cloud features should be able to encode rich geometry and
appearance cues and render realistic images, we train a point-cloud encoder
within a devised point-based neural renderer by comparing the rendered images
with real images on massive RGB-D data. The learned point-cloud encoder can be
easily integrated into various downstream tasks, including not only high-level
tasks like 3D detection and segmentation, but low-level tasks like 3D
reconstruction and image synthesis. Extensive experiments on various tasks
demonstrate the superiority of our approach compared to existing pre-training
methods.
