  Existing approaches for unsupervised point cloud pre-training are constrained
to either scene-level or point/voxel-level instance discrimination. Scene-level
methods tend to lose local details that are crucial for recognizing the road
objects, while point/voxel-level methods inherently suffer from limited
receptive field that is incapable of perceiving large objects or context
environments. Considering region-level representations are more suitable for 3D
object detection, we devise a new unsupervised point cloud pre-training
framework, called ProposalContrast, that learns robust 3D representations by
contrasting region proposals. Specifically, with an exhaustive set of region
proposals sampled from each point cloud, geometric point relations within each
proposal are modeled for creating expressive proposal representations. To
better accommodate 3D detection properties, ProposalContrast optimizes with
both inter-cluster and inter-proposal separation, i.e., sharpening the
discriminativeness of proposal representations across semantic classes and
object instances. The generalizability and transferability of ProposalContrast
are verified on various 3D detectors (i.e., PV-RCNN, CenterPoint, PointPillars
and PointRCNN) and datasets (i.e., KITTI, Waymo and ONCE).
