改进泊松算法的图像三维重建点云模型网格化

Point cloud gridding of 3D reconstruction from images based on the improved Poisson algorithm

针对泊松表面重建算法在点云数据的网格化中仍不能有效满足"细节保持与噪声平滑"的平衡问题,该文提出了一种基于高斯滤波的改进泊松算法。通过将高斯滤波引入到点云数据等值面的向量场估计中,一方面实现了对点云拓扑结构的更准确估计以及对点云噪声的有效平滑;另一方面通过调节高斯滤波中的标准差参数,实现了对点云模型网格化的细节保持与噪声平滑的细微控制。以福州大学张孤梅雕像为实验对象,图像三维重建技术获得的点云数据作为数据源,利用改进的泊松算法进行点云网格化。结果表明,改进的泊松算法提高了网格模型的精确性与完整性,且在视觉上更好地逼近真实模型的细节,验证了改进算法的有效性。

Since Poisson surface reconstruction algorithm still can＇t effectively meet the balance be- tween details keeping and noise smoothing, this paper presented an improved Poisson algorithm based on Gaussian filter. Through Gaussian filter in the vector field estimates of point cloud isosurface, it achieved a more accurate estimate of the point cloud topology and noise effectively smoothing. On the other side, by adjusting the standard deviation of the Gaussian filter, it realized a fine control of point cloud between details keeping and noise smoothing. The paper took the statue of Zhang Gumei in Fuzhou University as the experiment object, took point cloud from 3D multi-views reconstruction as input data, and carried out the point cloud gridding with improved poisson algorithm. The results showed that improved Poisson al- gorithm raised the accuracy and integrity of the mesh model and realized the approximation of the details keeping of the truth ground, which verified the effectiveness of the improved algorithm.