一个-最近的邻居指南为无监督点云注册的内在估计
IEEE transactions on neural networks and learning systems
|November 4, 2024
概括
本研究引入了一种新的无监督点云注册方法,使用几何结构一致性进行可靠的初始估计. 该方法通过利用双邻域匹配和转换不变表示来提高部分重叠场景的精度.
科学领域:
- 计算机视觉 计算机视觉
- 几何深度学习 几何深度学习
- 3D数据处理 3D数据处理
背景情况:
- 无监督点云的注册准确性受到不可靠的初始估计和自我监督信号的阻碍,特别是在部分重叠的情况下.
- 现有的方法在具有挑战性的注册场景中难以稳定地识别正确的对应.
研究的目的:
- 为无监督点云注册开发有效的初始估计方法.
- 通过捕捉几何结构的一致性来提高注册精度.
- 为无监督模型优化提供可靠的自我监督信号.
主要方法:
- 使用一个最接近邻近 (1-NN) 方法生成了高质量的参考点云副本.
- 集成的双邻居匹配分数 (1-NN和输入点云) 增强匹配的信心.
- 构建了变换不变的几何结构表示,以根据邻近图的一致性得分先前的信心.
- 采用加权的单值分解 (SVD) 算法进行转换估计.
主要成果:
- 拟议的方法通过利用几何结构的一致性来证明有效的初始估计.
- 双邻里匹配显著提高了匹配的信心和注册准确性.
- 转换不变表示提供可靠的自我监督信号,用于无监督训练.
- 在合成和现实世界数据集上的实验验证实了该方法的有效性.
结论:
- 拟议的无监督点云注册方法通过强大的初始估计实现了高精度.
- 捕捉几何结构一致性的策略为注册提供了一个强大的自我监督信号.
- 这种方法有效地解决了部分重叠点云注册的局限性.
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