@inproceedings{9b283ab3bc1c42419d239125ad32b851,
title = "3D reconstruction from non-uniform point clouds via local hierarchical clustering",
abstract = "Raw scanned 3D point clouds are usually irregularly distributed due to the essential shortcomings of laser sensors, which therefore poses a great challenge for high-quality 3D surface reconstruction. This paper tackles this problem by proposing a local hierarchical clustering (LHC) method to improve the consistency of point distribution. Specifically, LHC consists of two steps: 1) adaptive octree-based decomposition of 3D space, and 2) hierarchical clustering. The former aims at reducing the computational complexity and the latter transforms the non-uniform point set into uniform one. Experimental results on real-world scanned point clouds validate the effectiveness of our method from both qualitative and quantitative aspects.",
keywords = "3D reconstruction, hierarchical clustering, non-uniform, octree, Point cloud",
author = "Jiaqi Yang and Ruibo Li and Yang Xiao and Zhiguo Cao",
note = "Publisher Copyright: {\textcopyright} 2017 SPIE.; 9th International Conference on Digital Image Processing, ICDIP 2017 ; Conference date: 19-05-2017 Through 22-05-2017",
year = "2017",
doi = "10.1117/12.2281528",
language = "英语",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Xudong Jiang and Falco, {Charles M.}",
booktitle = "Ninth International Conference on Digital Image Processing, ICDIP 2017",
}