曲率感知点对签名用于强大的不平衡点云注册.
Xinhang Hu1, Zhao Zeng1, Jiwei Deng2
1School of Electronics and Control Engineering, Chang'an University, Xi'an 710064, China.
Sensors (Basel, Switzerland)
|October 29, 2025
概括
这项研究引入了一种新方法来记录不平衡的点云对,显著提高了准确性和效率. 该方法使用局部点集群结构特征来拒绝异常值,优于对基准的现有方法.
科学领域:
- 计算机视觉和几何处理计算机视觉和几何处理
- 3D点云注册 3D点云注册
背景情况:
- 现有的点云注册方法与不平衡的对 (不同的空间范围和点密度) 斗争.
- 由于尺度变化和不平衡点云中的密度分布不均,难以准确地进行本地注册.
研究的目的:
- 为不平衡的点云对开发一种新的注册方法.
- 为了应对点云注册中规模变化和不均密度的挑战.
主要方法:
- 在源点和目标点云中检测关键点.
- 使用本地特征描述符建立初始一对多对应.
- 采用一种新的局部点集群结构特征来拒绝异常值.
- 使用RANSAC.的转换假设生成和评估.
主要成果:
- 拟议的方法在注册成功率和计算效率方面明显优于KITTI-UPP基准的最先进方法.
- 在现实世界TIESY数据集上取得了竞争性结果,证明了适用性和通用性.
结论:
- 局部点集群结构特征在不平衡点云注册中对异常点拒绝有效.
- 拟议的方法为记录具有显著差异的点云提供了强大而高效的解决方案.
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