Fused Sampling and Grouping with Search Space Reduction for Efficient Point Cloud Acceleration

Recently, point-based deep neural networks (DNN) have demonstrated remarkable ability in analyzing point cloud data. However, challenges arise in sampling and grouping layers, particularly in terms of time and energy consumption due to the iterative access and computation of point cloud data for local feature extraction. In this paper, we introduce a Morton code-based data structure which stores point data with the shared upper bits together, enabling sequential access to the points within a specific voxel. We also propose a fused sampling and grouping approach with a reduced search space, which reuses the point data and the calculated distances for the farthest voxel and its neighbors. Additionally, a dedicated hardware architecture is introduced to maximize the efficiency of the proposed optimization technique. Experimental results show that our approach effectively reduces the number of distance calculations and data accesses with negligible accuracy loss, without requiring retraining of the network model.