From 40fbfffeaabb433afcd291a82693d56a7e005b3d Mon Sep 17 00:00:00 2001 From: Simon Giraudot Date: Mon, 12 Oct 2020 13:57:25 +0200 Subject: [PATCH] Update tutorial with scanline orientation --- .../Tutorials/Tutorial_reconstruction.txt | 18 +++++++++++++++--- 1 file changed, 15 insertions(+), 3 deletions(-) diff --git a/Documentation/doc/Documentation/Tutorials/Tutorial_reconstruction.txt b/Documentation/doc/Documentation/Tutorials/Tutorial_reconstruction.txt index fb60465bbc0..7a3986f3158 100644 --- a/Documentation/doc/Documentation/Tutorials/Tutorial_reconstruction.txt +++ b/Documentation/doc/Documentation/Tutorials/Tutorial_reconstruction.txt @@ -184,9 +184,21 @@ PCA is faster but jet is more accurate in the presence of high curvatures. These function only estimates the _direction_ of the normals, not their orientation (the orientation of the vectors might not be locally consistent). To properly orient the normals, the -function `mst_orient_normals()` can be used. Notice that it can also -be used directly on input normals if their orientation is not -consistent. +following functions can be used: + +- `mst_orient_normals()` +- `scanline_orient_normals()` + +The first one uses a _minimum spanning tree_ to consistently propagate +the orientation of normals in an increasingly large neighborhood. In +the case of data with many sharp features and occlusions (which are +common in airborne LIDAR data, for example), the second algorithm may +produce better results: it takes advantage of point clouds which are +ordered into scanlines to estimate the line of sight of each point and +thus to orient normals accordingly. + +Notice that these can also be used directly on input normals if their +orientation is not consistent. \snippet Poisson_surface_reconstruction_3/tutorial_example.cpp Normal estimation