为了提高点云表面的重建精度和准确度,针对泊松重建算法误连接孔洞区域及法线方向不一致导致重建结果偏差的问题,提出一种基于向量场和等值面的改进泊松重建算法。先利用统计滤波器对有噪声的点云数据进行去噪;再利用加权主成分分析估计法向并结合移动最小二乘(MLS)法计算点云法向和优化测量误差,利用OpenMP加速法线估计;最后利用改进DC(Dual Contouring)算法提取等值面来消除曲面孔洞和误连接曲面特征的问题。经过实验验证,改进的泊松算法可有效地去除模型中可能存在的孔洞问题和伪封闭曲面,提高重建曲面的准确度和效率。

We propose an improved Poisson reconstruction algorithm based on a vector field and an isosurface to improve the precision and accuracy associated with point cloud surface reconstruction. The proposed algorithm intends to solve the following problems: the Poisson reconstruction algorithm misconnects the empty regions and different normal directions cause the deviation of the reconstruction results. Initially, a statistical filter was used to denoise the noisy point cloud data. Subsequently, the weighted principal component analysis method was used to estimate the normal direction, and the moving least squares (MLS) method was used to calculate and optimize the measurement error associated with the point cloud normal. Further, OpenMP was used for accelerating the proposed method. Finally, the improved dual contouring algorithm was used for extracting the isosurface to eliminate the problems of surface empty regions and misconnected surface features. The experimental results demonstrate that the improved Poisson algorithm can effectively eliminate the possible empty regions and pseudo-enclosed surfaces in the model and improve the accuracy as well as efficiency of the surface reconstruction.