JointPruning: Pruning Networks Along Multiple Dimensions for Efficient Point Cloud Processing

Abstract

Deep neural networks designed for point clouds, also called point cloud neural networks (PCNNs), are attracting increasing attention in recent years. In this work, we propose the first model compression framework referred to as JointPruning (JP) that is specifically designed for compressing PCNNs. Observing that the redundancies in PCNNs are largely affected by certain parameters like the number of points, we first propose a new search space specifically designed for PCNNs. By searching the optimal pruning policy in our newly proposed search space, our JP framework can prune the PCNNs at different levels and simultaneously reduce the redundancies along multiple dimensions. As the newly proposed search space consists of multiple levels and the policy value at each level is continuous in our JP framework, it is hard to directly search for the best pruning policy in such a large search space. To this end, we further propose two strategies called search space refinement and validation set extension to progressively refine the granularity of our searching process in a coarse-to-fine and easy-to-hard fashion, which can help us gradually find better pruning policies. Comprehensive experiments on three benchmark datasets demonstrate the effectiveness of our JP framework for compressing PCNNs.