自适应精简点云改进预处理优化三维重建算法

Improved Preprocessing and Optimized 3D Reconstruction Algorithm of Adaptive Simplified Point Cloud

针对初始点云离群点噪声大、冗余性高导致三维重建效率低、重建曲面表面粗糙等问题,提出一种自适应精简点云改进预处理算法。首先使用统计滤波消除离群点噪声,并在基于体素重心邻近特征点下采样中引入双曲正切函数,在保持点云特征不变的情况下精简点云数据;然后建立移动最小二乘法拟合函数,确定其二次基函数和高斯权函数,完成点云数据平滑优化;最后使用投影三角化算法完成点云曲面重建。实验结果表明,所提算法在有效去除离群点的同时,还能精简点云数据、提升曲面重建效率,且重建后的模型表面光滑、孔洞减少。

High noise and redundancy of outliers in the initial point cloud result in low efficiency of three-dimensional reconstruction and rough surface of reconstructed surface.Thus,this study proposes an improved preprocessing algorithm for an adaptive simplified point cloud.Statistical filtering was used to eliminate outlier noise,and the hyperbolic tangent function was introduced into the downsampling based on the voxel center of gravity adjacent feature points to maintain the point cloud features and simplify the point cloud data.The moving least square fitting function was then established.Furthermore,its quadratic basis function and Gaussian weight function were determined,and the point cloud data was smoothed and optimized.Finally,the projection triangulation algorithm was used to reconstruct the point cloud surface.Experimental results show that the proposed algorithm can effectively remove outliers,simplify point cloud data,and improve the efficiency of surface reconstruction,and the reconstructed model has a smooth surface and fewer holes.