计算机应用 ›› 2016, Vol. 36 ›› Issue (12): 3394-3397.DOI: 10.11772/j.issn.1001-9081.2016.12.3394

• 虚拟现实与数字媒体 • 上一篇    下一篇

散乱点云的自适应α-shape曲面重建

何华1, 李宗春1, 李国俊2, 阮焕立1, 隆昌宇3   

  1. 1. 信息工程大学 地理空间信息学院, 郑州 450001;
    2. 北京卫星导航中心, 北京 100094;
    3. 北京卫星环境工程研究所, 北京 100094
  • 收稿日期:2016-06-16 修回日期:2016-09-06 出版日期:2016-12-10 发布日期:2016-12-08
  • 通讯作者: 何华
  • 作者简介:何华(1992-),男,重庆人,硕士研究生,主要研究方向:点云曲面重建、精密工程测量;李宗春(1973-),男,山东日照人,教授,博士,主要研究方向:精密工程测量;李国俊(1990-),男,湖北武穴人,助理工程师,硕士,主要研究方向:时频计量、点云曲面重建;阮焕立(1991-),男,河南商丘人,硕士研究生,主要研究方向:点云拼接;隆昌宇(1988-),男,山东东营人,工程师,博士,主要研究方向:航天器精度测量。
  • 基金资助:
    国家自然科学基金资助项目(41274014);航天器高精度测量联合实验室基金资助项目(201501)。

Surface reconstruction for scattered point clouds with adaptive α-shape

HE Hua1, LI Zongchun1, LI Guojun2, RUAN Huanli1, LONG Changyu3   

  1. 1. Institute of Geography Space Information, Information Engineering University, Zhengzhou Henan 450001, China;
    2. Beijing Satellite Navigation Center, Beijing 100094, China;
    3. Beijing Institute of Spacecraft Environment Engineering, Beijing 100094, China
  • Received:2016-06-16 Revised:2016-09-06 Online:2016-12-10 Published:2016-12-08
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (41274014), the Foundation of High-Precision Measuring Joint Laboratory for Spacecraft (201501).

摘要: 针对α-shape算法不适用于散乱非均匀点集曲面重建的问题,提出了一种基于点云数据局部特征尺寸(LFS)的自适应α-shape曲面重建改进算法。首先,以采样点的k-邻近点计算出负极点逼近曲面中轴(MA);然后,根据近似中轴计算曲面在采样点处的局部特征尺寸,并依据局部特征尺寸对原始点云进行非均匀降采样;最后,根据三角面片的外接球半径和对应的α值自适应重建出物体表面。与α-shape算法相比,所提算法可以有效合理地减少点云数据量,点云简化率达到70%左右,同时重建结果中冗余三角面片更少且基本没有孔洞。实验结果表明,所提算法能够自适应地重建出非均匀点集的表面。

关键词: α-shape算法, 局部特征尺寸, 曲面重建, 点云简化, 自适应

Abstract: The α-shape algorithm is not suitable for surface reconstruction of scattered and non-uniformly sampled points. In order to solve the problem, an improved surface reconstruction algorithm with adaptive α-shape based on Local Feature Size (LFS) of point cloud data was proposed. Firstly, Medial Axis (MA) of the surface was approximated by the negative poles computed by k-nearest neighbors of sampled points. Secondly, the LFS of sampled points was calculated by the approximated MA, and the original point clouds were unequally simplified based on LFS. Finally, the surface was adaptively reconstructed based on the radius of circumscribed ball of triangles and the corresponding α value. In the comparison experiments with α-shape algorithm, the proposed algorithm could effectively and reasonably reduce the number of point clouds, and the simplification rate of point clouds achieved about 70%. Simultaneously, the reconstruction result were obtained with less redundant triangles and few holes. The experimental results show that the proposed algorithm can adaptively reconstruct the surface of non-uniformly sampled point clouds.

Key words: α-shape algorithm, Local Feature Size (LFS), surface reconstruction, point clouds simplification, adaptive

中图分类号: