DFPS:一个高效的低采样算法,旨在保护大规模点云数据的全球特征
Jiahui Dong1, Maoyi Tian1, Jiayong Yu2
1College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
本研究介绍了一种高效的3D点云下采样算法 (DFPS),可以显著加快处理速度,同时保留全球特征. DFPS为大规模3D数据提供了一个轻量级的解决方案,可适应各种设备.
科学领域:
- 计算机视觉 计算机视觉
- 3D数据处理 3D数据处理
- 计算几何学的计算几何学
背景情况:
- 大规模的3D点云数据集为处理和分析带来了重大的计算挑战.
- 现有的减量采样方法往往难以平衡效率与保护关键的全球特征.
- 越来越多的3D数据需要更高效的算法,用于诸如自动驾驶和增强现实等应用.
研究的目的:
- 引入一个高效和自适应的3D点云下采样算法 (DFPS).
- 为了证明DFPS保持全球特征的能力,同时显著提高处理速度.
- 为在资源有限的环境中提供可部署的轻量级解决方案.
主要方法:
- 开发了一个算法 (DFPS) 使用自适应的多层格子分区.
- 实现了多线程并行加速架构,以提高处理速度.
- 用于对特征保存的定量分析的法距离.
主要成果:
- 与FPS相比,在12.5%的采样率 (71.64s vs 161,665s) 实现了超过2200倍的效率改进.
- 在3.125%的采样率下,证明了近10,000倍的效率提高.
- 通过视觉和定量分析验证了全球特征信息的有效保存.
结论:
- DFPS为3D点云下采样提供了高效和有效的解决方案.
- 该算法的自适应性和并行架构使其适合大规模数据集.
- 它的GPU独立设计允许在各种设备上灵活部署,支持下游任务,如注册和细分.
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