Normal Estimation in Unstructured Point Clouds with Hough transform
Please acknowledge our reference paper :
"Fast and robust normal estimation for point clouds with sharp features" by Alexandre Boulch and Renaud Marlet, Symposium on Geometry Processing 2012 (SGP 2012) and Computer Graphics Forum
The code for normal estimation is C++ hearder only. Three version are proposed, previous version are located in
archives/pcl/ relies on CGAL and PCL libriaries.
The current version Normals.h relies on Eigen and nanoflann (we provide them in the
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.
Examples and tests
Using the library with Python requires building the wrapper. All python code has been built and tested under Unbuntu 18.04 and Anaconda.
Building the wrapper
cd python python setup.py install --home="."
From the root directory:
The script creates a point cloud on a cube and estimate the normals.
It produces a
.xyz containing both points and normals, it can be displayed using Meshlab or CloudCompare.
In the folder
test_cpp, the executable can be generated using CMake.
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