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

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.