Putting 3D Spatially Sparse Networks on a Diet

3D neural networks have become prevalent for many 3D vision tasks including object detection, segmentation, registration, and various perception tasks for 3D inputs. However, due to the sparsity and irregularity of 3D data, custom 3D operators or network designs have been the primary focus of 3D research, while the size of networks or efficacy of parameters has been overlooked. In this work, we perform the first comprehensive study on the weight sparsity of spatially sparse 3D convolutional networks and propose a compact weight-sparse and spatially sparse 3D convnet (WS^3-ConvNet) for semantic segmentation and instance segmentation. We employ various network pruning strategies to find compact networks and show our WS^3-ConvNet achieves minimal loss in performance (2.15% drop) with orders-of-magnitude smaller number of parameters (1/100 compression rate). Finally, we systematically analyze the compression patterns of WS^3-ConvNet and show interesting emerging sparsity patterns common in our compressed networks to further speed up inference.