  Accurately detecting objects in the environment is a key challenge for
autonomous vehicles. However, obtaining annotated data for detection is
expensive and time-consuming. We introduce PatchContrast, a novel
self-supervised point cloud pre-training framework for 3D object detection. We
propose to utilize two levels of abstraction to learn discriminative
representation from unlabeled data: proposal-level and patch-level. The
proposal-level aims at localizing objects in relation to their surroundings,
whereas the patch-level adds information about the internal connections between
the object's components, hence distinguishing between different objects based
on their individual components. We demonstrate how these levels can be
integrated into self-supervised pre-training for various backbones to enhance
the downstream 3D detection task. We show that our method outperforms existing
state-of-the-art models on three commonly-used 3D detection datasets.
