Not All Neighbors Matter: Point Distribution-Aware Pruning for 3D Point Clouds

Applying ddeep neural networks to 3D point cloud processing has shown a rapid pace of advancements in domains where 3D geometry information can greatly boost task performance, such as AR/VR, robotics, and autonomous driving. However, along with rapid increase in the size of the neural network models and 3D point clouds, reducing the entailed computation and memory access overhead is a primary challenge to meet strict latency and energy requirements of practical applications. This paper proposes a new technique called spatial point distribution aware pruning by leveraging spare nature of 3D point could processing. We identified that particular groups of neighborhood voxels in 3D point clouds more frequently contribute to actual output features. We propose to selectively prune less contributing groups of neighborhood voxels first to reduce the computation overhead while reducing impacts on model accuracy. We applied our technique to three representative Sparse 3D Convolution libraries and showed that our technique significantly reduces the inference latency by 1.48× and energy consumption by 1.61× on NVIDIA GV100 without any accuracy loss.

• Machine Perception

• Machine Intelligence