通过预测采样点距离曲线，加速3D点云处理中的采样与搜索。
FastPoint: Accelerating 3D Point Cloud Model Inference via Sample Point Distance Prediction
- 利用采样点间距离的可预测性，避免全部距离计算。
- 在RTX 3090上实现2.55倍端到端推理加速。
- 适合需要高效处理大规模点云的应用场景。
深度神经网络已彻底改变3D点云处理，但高效处理大规模不规则点云仍具挑战。为解决此问题，我们提出FastPoint，一种基于软件的加速技术，利用远点采样过程中采样点间距离的可预测趋势。通过预测距离曲线，可高效定位后续采样点，无需计算所有成对距离。该方法显著加速远点采样与邻域搜索操作，同时保持采样质量与模型性能。将FastPoint集成至先进3D点云模型后，在NVIDIA RTX 3090 GPU上实现2.55倍端到端速度提升，且未牺牲精度。
原文摘要 · Abstract (English)
Deep neural networks have revolutionized 3D point cloud processing, yet efficiently handling large and irregular point clouds remains challenging. To tackle this problem, we introduce FastPoint, a novel software-based acceleration technique that leverages the predictable distance trend between sampled points during farthest point sampling. By predicting the distance curve, we can efficiently identify subsequent sample points without exhaustively computing all pairwise distances. Our proposal substantially accelerates farthest point sampling and neighbor search operations while preserving sampling quality and model performance. By integrating FastPoint into state-of-the-art 3D point cloud models, we achieve 2.55x end-to-end speedup on NVIDIA RTX 3090 GPU without sacrificing accuracy.
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