ObjectContrast: Self-supervised Point Cloud Pre-training via Object Feature Contrast

Current point cloud object detection methods rely on expensive manual annotation. Utilizing contrastive learning for self-supervised pre-training on unlabeled large-scale point clouds can reduce annotation costs and improve model performance. However, selecting effective features for instance discrimination is crucial for contrastive learning. Previous methods have constructed instances for pre-training at different levels, such as points, proposals, and scenes, but the features of these instances differ from the objects to be detected. Considering that instance discrimination tasks based on object-level features align with downstream object detection tasks, we propose a novel and efficient self-supervised point cloud object detection pre-training framework called ObjectContrast. To learn more effective point cloud representations, this framework constructs two self-supervised pre-training modules: object-level instance discrimination contrast ObCo and bounding box geometric contrast prediction BoxCo . ObCo drives the model to learn general object representations to locate object foregrounds and determine categories. BoxCo enhances the model's geometric perception capabilities regarding the dimension and orientation of 3D bounding boxes. Extensive experiments on various detectors and datasets validate the efficiency and transferability of ObjectContrast. Compared with the state-of-the-art self-supervised pre-training methods, ObjectContrast demonstrates superior performance.