Edge-Aware Point Set Resampling
Edge-Aware Point Set Resampling
复制标题
DOI：
10.1145/2421636.2421645
复制
发表时间：
2013-01-01
影响因子：
6.2
通讯作者：
Zhang, Hao (Richard)
中科院分区：
文献类型：
--
作者：
Huang, Hui;Wu, Shihao;Zhang, Hao (Richard)
激光扫描仪获取的点本身并不具有法线，而法线对于曲面重建和使用曲面的点集绘制是必不可少的。正态估计对噪声非常敏感。在尖锐特征附近，由于边缘奇异点处固有的欠采样问题，无噪声法线的计算变得更具挑战性。因此，常见的边缘感知合并技术（如双边平滑）仍可能在边缘附近产生错误的法线。我们提出了一个restrial的方法来处理一个嘈杂的，可能离群的点集在边缘感知的方式。我们的关键思想是首先从边缘重新采样，以便可以在样本处计算可靠的法线，然后基于可靠的数据，我们逐步重新采样点集，同时接近边缘奇点。我们证明，我们的边缘感知呼吸（ESTA）算法是能够产生巩固的点集与无噪声的法线和干净的保存尖锐的功能。我们还表明，边缘感知重建方法和点集渲染技术的性能得到改善。
Points acquired by laser scanners are not intrinsically equipped with normals, which are essential to surface reconstruction and point set rendering using surfels. Normal estimation is notoriously sensitive to noise. Near sharp features, the computation of noise-free normals becomes even more challenging due to the inherent undersampling problem at edge singularities. As a result, common edge-aware consolidation techniques such as bilateral smoothing may still produce erroneous normals near the edges. We propose a resampling approach to process a noisy and possibly outlier-ridden point set in an edge-aware manner. Our key idea is to first resample away from the edges so that reliable normals can be computed at the samples, and then based on reliable data, we progressively resample the point set while approaching the edge singularities. We demonstrate that our Edge-Aware Resampling (EAR) algorithm is capable of producing consolidated point sets with noise-free normals and clean preservation of sharp features. We also show that EAR leads to improved performance of edge-aware reconstruction methods and point set rendering techniques.