诺雷斯特-Net:用于3D噪点点云的正常估计神经网络.
IEEE transactions on neural networks and learning systems
|January 25, 2024
概括
我们介绍Norest-Net,这是一个新的神经网络,用于准确的3D点云正常估计. 它将噪声过和特征保存分开,改善噪声数据的结果.
科学领域:
- 计算机视觉 计算机视觉
- 3D数据处理 3D数据处理
- 机器学习 机器学习
背景情况:
- 来自像LiDAR和深度摄像头这样的传感器的3D点云通常很.
- 准确的正常估计对于下游3D处理任务至关重要.
- 现有的方法难以平衡降噪和特征保护.
研究的目的:
- 为杂的3D点云开发一个强大的正常估计方法.
- 为了同时提高噪声过和表面特征保护.
- 为增强现有的正常估计技术提供一个模块化组件.
主要方法:
- 提出了Norest-Net,一个神经网络,有两个专门的分支:NF-Net用于噪声过和NR-Net用于功能改进.
- NF-Net学会从杂的高度地图描述器中预测地面真相正常值.
- 通过双边过的点正常描述器,NR-Net学会预测基准真相正常值.
主要成果:
- 在正常估计准确度方面,Norest-Net显著超过了最先进的方法.
- 该方法在保护表面特征方面表现出卓越的性能.
- 在合成数据和现实数据中,Norest-Net表现出对噪声的增强强性.
结论:
- 在正常估计中,Norest-Net有效地解决了噪声过和特征保存之间的权衡.
- 拟议的架构为3D点云处理提供了一种专业化和改进的方法.
- 可拆卸的NR-Net模块可以提高现有的正常估计算法的性能.
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