针对密集点云的快速曲面重建算法

为了能够快速地从高密度散乱点云生成三角形网格曲面,提出一种针对散乱点云的曲面重建算法.首先通过逐层外扩建立原始点云的近似网格曲面,然后对近似网格曲面进行二次剖分生成最终的精确曲面;为了能够处理噪声点云,在剖分过程中所有网格曲面顶点都通过层次B样条进行了优化.相比于其他曲面重建方法,该算法剖分速度快,且能够保证点云到所生成的三角网格曲面的距离小于预先设定容限.实验结果表明,文中算法能够有效地实现高密度散乱点云的三角剖分,且其剖分速度较已有算法有大幅提高.

Rapid Surface Reconstruction Algorithm from Dense Point Cloud

In order to triangulate a dense scattered point cloud efficiently,a novel surface reconstruction method is proposed.The algorithm presented consists of two steps: Firstly,an initial triangle mesh is constructed by repeating a simple advancing front rule.Then,initial triangles are subdivided to obtain the final accurate surface and triangle vertexes are refined by means of Multilevel B-spline fitting.Compared with other popular methods,the subdivide speed is a significant advantage of the algorithm.Besides,the algorithm guarantees that the distance from original points to the result mesh is within a predefined tolerance.Several experiments based on real scan data are used to evaluate the efficiency of this algorithm and the speed advantage is demonstrated by the comparison with other popular algorithms.