基于误差驱动与CSRBF的点云重建

提出了一种基于误差驱动的逐次迭代逼近的大规模3D散乱数据的重建算法。首先对点云数据进行重采样，采用归一化的CSRBF作为插值基函数。其次对重采样后少量的点数据进行插值。再次对未参加插值的点带入隐函数方程，计算误差。对误差超过一定阈值的点进行重采样，加入原采样点集合，重新进行插值。这样多次迭代以后便可以用最少的点来插值原来的点云模型。实验结果表明，该算法具有更高的鲁棒性和更高的效率。

Point cloud reconstruction based on error driven and compactly supported radial basis function

This paper puts forward a reconstruction algorithm based on the error driver,the successive iterative approximation and large-scale 3D scattered data.This paper re-samples the point cloud data,using normalized CSRBF as interpolation function(RBF),then interpolates to a small amount of points data which has been re-sampled.After that the points that don't attend interpolation will be put into the implicit function equation to calculate error.The points whose error beyond certain threshold will be re-sampled,...