  This paper tries to address a fundamental question in point cloud
self-supervised learning: what is a good signal we should leverage to learn
features from point clouds without annotations? To answer that, we introduce a
point cloud representation learning framework, based on geometric feature
reconstruction. In contrast to recent papers that directly adopt masked
autoencoder (MAE) and only predict original coordinates or occupancy from
masked point clouds, our method revisits differences between images and point
clouds and identifies three self-supervised learning objectives peculiar to
point clouds, namely centroid prediction, normal estimation, and curvature
prediction. Combined with occupancy prediction, these four objectives yield an
nontrivial self-supervised learning task and mutually facilitate models to
better reason fine-grained geometry of point clouds. Our pipeline is
conceptually simple and it consists of two major steps: first, it randomly
masks out groups of points, followed by a Transformer-based point cloud
encoder; second, a lightweight Transformer decoder predicts centroid, normal,
and curvature for points in each voxel. We transfer the pre-trained Transformer
encoder to a downstream peception model. On the nuScene Datset, our model
achieves 3.38 mAP improvment for object detection, 2.1 mIoU gain for
segmentation, and 1.7 AMOTA gain for multi-object tracking. We also conduct
experiments on the Waymo Open Dataset and achieve significant performance
improvements over baselines as well.
