Multi-Dimensional Pruned Sparse Convolution for Efficient 3D Object Detection

In recent years, significant progress has been made in 3D object detection. The focus of research has primarily been on improving the detection accuracy of models, however, neglecting their efficiency during actual deployment. Aiming at this issue, in this paper, we propose a multi-dimensional pruning method from the perspectives of data and model. Specifically, given the input data represented by the voxel grid, we first measure the voxel importance and propose an importance-based sampling module to sparsify voxels while preserving informative ones. The model pruning is wrapped in the framework of weighted voxel distillation, where the student model is obtained by pruning the channels of teacher model and only the informative voxels in the teacher model are involved and transferred to the students. In addition, the proposed method can be seamlessly integrated into current voxel-based 3D detectors without any additional costs. Experimental results on the KITTI and ONCE datasets show that our method can achieve a reduction of over 80% in GFLOPs while maintaining superior performance.