Normal Estimation in Unstructured Point Clouds with Hough transform
Please acknowledge our the reference paper :
"Deep Learning for Robust Normal Estimation in Unstructured Point Clouds " by Alexandre Boulch and Renaud Marlet, Symposium of Geometry Processing 2016, Computer Graphics Forum
The code for normal estimation is C++ hearder only. Three version are proposed, previous version are located in cgal/ en pcl/ relies on CGAL and PCL libriaries. The current version Normals.h relies on Eigen and nanoflann (assumed to be in the source folder when compiling).
neighborhood_size (default 200)the neighborhood size for computing the normals.n_planes (default 700)the number of random sample to be picked to estimate the distribution.n_rot (default 5)the number of random rotations of the accumulator.n_phi (default 15)the discretization of the sphere accumulator.tol_angle_rad (default 0.79)the maximal angle used for normal cluster (final normal decision).k_density (defautl 5)the neighborhood size for density computation.
Hough_Exec [options] -i input_file.xyz -o output_file.xyz
The code is released under a GPLv3 license. For commercial purposes contact the authors. The detailed licence is here.
Previous version of the code are located in folders cgal/ and pcl/