Efficient Sampling and Grouping Acceleration for Point Cloud Deep Learning via Single Coordinate Comparison

With the focus on three-dimensional (3D) applications, the importance of applying deep learning to point clouds have been growing recently. It is known that mapping operations including sampling and grouping play a critical role in extracting local features in point-based deep learning models. However, the mapping operations often become bottlenecks in terms of computing times due to the repetitive comparison of distances between input points. In this paper, we analyzed the characteristics of distance distribution during sampling and grouping operations, and discovered that substantial portion of the distance comparison does not need exact 3D Euclidean distance using all three coordinates. Based on the observations, we propose a technique called single coordinate comparison which selectively determines the comparison output with 1D-distance only. We also present a hardware architecture with a distance calculator capable of handling both 3D and 1D distance. The experimental results demonstrate the effectiveness of our approach in reducing both time and energy consumption, particularly as the number of points increases.