Title: Self-adaptive polygon mesh reconstruction based on ball-pivoting algorithm

Authors: Yi An; Peng Zhao; Zhuohan Li; Cheng Shao

Addresses: School of Control Science and Engineering, Dalian University of Technology, No.2 Linggong Road, Dalian, Liaoning 116023, China ' School of Control Science and Engineering, Dalian University of Technology, No.2 Linggong Road, Dalian, Liaoning 116023, China ' School of Control Science and Engineering, Dalian University of Technology, No.2 Linggong Road, Dalian, Liaoning 116023, China ' School of Control Science and Engineering, Dalian University of Technology, No.2 Linggong Road, Dalian, Liaoning 116023, China

Abstract: Accurate polygon mesh reconstruction from point cloud data is a key technique in many application domains, such as reverse engineering, computer vision, industrial inspection, and pattern recognition. In this paper, we propose a new method for reconstructing the polygon mesh from point cloud data based on the ball-pivoting algorithm. The core idea of the proposed method is to determine a suitable radius of the pivoting ball according to the principles of density self-adaption and balance distribution, which will greatly improve the accuracy and efficiency of the polygon mesh reconstruction from point cloud data, especially from uneven point cloud data. The proposed method is evaluated and compared with other existing methods in the experiments with several real point cloud datasets. The experimental results show that the proposed method has good performance, and it is robust to the uneven distribution of point cloud.

Keywords: surface reconstruction; polygon mesh; point cloud; density self-adaption; balance distribution; ball pivoting algorithm.

DOI: 10.1504/IJCAT.2016.077790

International Journal of Computer Applications in Technology, 2016 Vol.54 No.1, pp.51 - 60

Received: 13 Jan 2015
Accepted: 02 Mar 2015
Published online: 15 Jul 2016 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article