论文标题
在功能保留点云过滤中朝着均匀的点分布
Towards Uniform Point Distribution in Feature-preserving Point Cloud Filtering
论文作者
论文摘要
作为3D数据的流行表示，点云可能包含噪声，并且需要在使用前进行过滤。现有的点云过滤方法不能保留尖锐的特征，或者导致过滤后输出中的点分布不均匀。为了解决此问题，本文介绍了一种点云过滤方法，该方法考虑了滤波过程中的点分布和特征保存。关键思想是在能量最小化中纳入具有数据项的排斥项。排斥项是负责点分布的原因，而数据项是在保留几何特征的同时近似嘈杂的表面。此方法能够处理具有精细功能和尖锐特征的模型。广泛的实验表明，我们的方法在几秒钟内平均得出更均匀的点分布（$ 5.8 \ times10^{ - 5} $倒角距离），从而产生更好的结果。
As a popular representation of 3D data, point cloud may contain noise and need to be filtered before use. Existing point cloud filtering methods either cannot preserve sharp features or result in uneven point distribution in the filtered output. To address this problem, this paper introduces a point cloud filtering method that considers both point distribution and feature preservation during filtering. The key idea is to incorporate a repulsion term with a data term in energy minimization. The repulsion term is responsible for the point distribution, while the data term is to approximate the noisy surfaces while preserving the geometric features. This method is capable of handling models with fine-scale features and sharp features. Extensive experiments show that our method yields better results with a more uniform point distribution ($5.8\times10^{-5}$ Chamfer Distance on average) in seconds.
