研究基于点正常约束的3D点云图学习算法
Zhao Fang1, Youyu Liu1, Lijin Xu2
1School of Mechanical and Automotive Engineering, Anhui Polytechnic University, Wuhu 241000, China.
Sensors (Basel, Switzerland)
|October 16, 2024
概括
这项研究引入了一种新的特征地图学习算法,以消除激光点云,显著提高准确性并保留本地几何. 该方法有效地减少噪音,增强表面重建和可视化过程.
科学领域:
- 计算机视觉 计算机视觉
- 几何处理 几何处理
- 信号处理 信号处理
背景情况:
- 激光点云易受高斯和拉普拉斯噪声的影响,降低了表面重建和可视化准确度.
- 现有的无色化方法往往不考虑点云正常向量的局部一致性和密度.
研究的目的:
- 开发一个先进的点云消除算法,解决当前方法的局限性.
- 为了提高激光点云的精度,稳定性和效率.
主要方法:
- 一个特征地图学习算法,集成点正常约束,迪里克莱特能量和合直角偏差术语.
- 使用迪里克莱特能量来惩罚邻近的正常向量之间的差异.
- 整合点云密度函数以捕捉本地特征相关性并减轻混合噪声.
主要成果:
- 与MRPCA和NLD算法相比,平均平均平方误差 (MSE) 减少了0.005和0.054.与MRPCA和NLD算法相比,平均平方误差 (MSE) 减少了0.005和0.054.
- 与MRPCA和AWLOP相比,平均信号噪声比 (SNR) 提高了0.13dB和2.14dB.
- 与RSLDM方法相比,计算效率提高了27%.
结论:
- 拟议的算法有效地消除了激光点云中的混合噪声,同时保留了本地基本的几何特征.
- 该方法在准确性,稳定性和计算效率方面表现出比现有技术更高的性能.
- 这种方法为依赖高质量的点云数据的应用提供了显著的进步.
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