VoxelContrast: Voxel Contrast-Based Unsupervised Learning for 3D Point Clouds

The annotation process for 3D point cloud data is more complex than for image data, and training with a small amount of annotated data significantly reduces the performance of deep learning models. Unsupervised learning can better utilize large amounts of unlabeled point cloud data for model pretraining, thereby achieving excellent performance on small-scale datasets. However, many existing 3D point cloud unsupervised learning methods are primarily focused on single-object CAD point clouds and may not be suitable for larger-scale autonomous driving LiDAR point clouds. To address this challenging problem, we propose a voxel contrast-based unsupervised learning method (VoxelContrast), which adapts well to different types of point cloud data through voxelization and can be seamlessly integrated with existing model frameworks. Specifically, we utilize voxelization methods to preprocess point cloud data. Then, we incorporate voxel information into contrastive learning, facilitating the creation of more meaningful positive and negative sample pairs. Finally, we conduct unsupervised training of the model using instance discrimination as the proxy task. Our method was validated in two downstream tasks: point cloud shape classification and 3D object detection. Experimental results demonstrated that models pretrained using a substantial amount of unlabeled data can further enhance the effectiveness of existing supervised learning methods.