Efficient Point Cloud Analytics on Edge Devices

Point clouds are crucial for 3D geometry representation, and vital in applications like autonomous driving and augmented reality. Despite advancements in deep learning-based analytics, their high computational cost limits deployment on edge devices with constrained resources. To this end, we analyze PointNet++, a leading point cloud analytics framework, identifying two major bottlenecks: 1) GPU is underutilized due to limited parallelism and excessive kernel launches in the sampling stage and voting stage, and 2) irregular memory accesses in the grouping stage. To address these, we propose parallel sampling and voting to enhance GPU utilization and fuse subroutines in grouping to improve memory efficiency. Experimental results demonstrate that our optimizations result in significant speedup (up to $5.0 \times, 3.2 \times$ on average) across various point cloud workloads on edge devices.