Adaptive Mesh Reconstruction of Point Cloud with Feature Preserved
保持特征的点云自适应网格重建

There is noise and defective data on the 3D scanning point cloud. A robust mesh reconstruction algorithm is proposed. Surface normals are estimated by tensor matrix with enhanced features. By computing 3D fast Fourier transform (FFT), discrete iso-surface is extracted. Points are moved onto the iso-surface by an iterative clustering along gradient field, where the noise and outliers are removed and defective data are repaired. Point cloud is decimated adaptively, and then a new triangle is generated using sphere-intersected method. The experimental results have shown that the algorithm is fast, robust and use low memory.