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arXiv · 2309.05017

FuseFPS: Accelerating Farthest Point Sampling with Fusing KD-tree Construction for Point Clouds

Meng Han·Liang Wang·Limin Xiao·Hao Zhang·Chenhao Zhang·Xilong Xie·Shuai Zheng·Jin Dong
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Abstract

Point cloud analytics has become a critical workload for embedded and mobile platforms across various applications. Farthest point sampling (FPS) is a fundamental and widely used kernel in point cloud processing. However, the heavy external memory access makes FPS a performance bottleneck for real-time point cloud processing. Although bucket-based farthest point sampling can significantly reduce unnecessary memory accesses during the point sampling stage, the KD-tree construction stage becomes the predominant contributor to execution time. In this paper, we present FuseFPS, an architecture and algorithm co-design for bucket-based farthest point sampling. We first propose a hardware-friendly sampling-driven KD-tree construction algorithm. The algorithm fuses the KD-tree construction stage into the point sampling stage, further reducing memory accesses. Then, we design an efficient accelerator for bucket-based point sampling. The accelerator can offload the entire bucket-based FPS kernel at a low hardware cost. Finally, we evaluate our approach on various point cloud datasets. The detailed experiments show that compared to the state-of-the-art accelerator QuickFPS, FuseFPS achieves about 4.3$\times$ and about 6.1$\times$ improvements on speed and power efficiency, respectively.

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Meng Han, Liang Wang, Limin Xiao, Hao Zhang, Chenhao Zhang, Xilong Xie, Shuai Zheng, Jin Dong. 2023-09-10. FuseFPS: Accelerating Farthest Point Sampling with Fusing KD-tree Construction for Point Clouds. https://doi.org/10.1109/asp-dac58780.2024.10473914

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