Abstract
Mask-based pre-training has achieved great success for self-supervised learning in image, video and language, without manually annotated supervision. However, as information redundant data, it has not yet been studied in the field of 3D object detection. As the point clouds in 3D object detection is large-scale, it is impossible to reconstruct the input point clouds. In this paper, we propose a mask voxel classification network for large-scale point clouds pre-training. Our key idea is to divide the point clouds into voxel representations and classify whether the voxel contains point clouds. This simple strategy makes the network to be voxel-aware of the object shape, thus improving the performance of 3D object detection. Extensive experiments show great effectiveness of our pre-trained model with 3D object detectors (SECOND, CenterPoint, and PV-RCNN) on three popular datasets (KITTI, Waymo, and nuScenes). Codes are publicly available at https: //github.com/chaytonmin/Voxel-MAE.
Abstract (translated)
URL
https://arxiv.org/abs/2206.09900
