  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 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 and instance
segmentation on the real-world indoor and outdoor datasets. 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 (99\% compression rate) and computational cost
(95\% reduction). 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 (45\% faster).
\keywords{Efficient network architecture, Network pruning, 3D scene
segmentation, Spatially sparse convolution}
