I have a point cloud (data set) (3D) representing an urban terrain consisting of flat roof surfaces (of buildings) . My aim is to figure out the flat surfaces , waterbodies from the given data set .The data set is a text file consisting of the number of points followed by their individual x , y , z co-ordinates. As a trial attempt , I have generated the 2D-Delaunay triangulation of the given data set to get the triangulated surface. Henceforth, I plan to execute a graph-traversal over the faces of the triangulation to look for neighbourhood points with nearly the same z-coordinate value and treat them as a flat surface . I am using CGAL libraries to accomplish the same in C++. Is there a better approach for identifying flat surface or my method seems decent enough ?
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I think this question is formulated too broadly...do you want comments on your approach? Or are you looking for the best algo somebody else knows about? The delaunay-triangulation part sounds nice. But still, I'm not very convinced by your approach - For instance how are you going to decide what 'nearly' is? And then: very inclined surfaces will have very different z coordinates but still are flat. You probably should rather look at the inclination of the triangles and compare those. Then again computational geometry is not my strength...
- dingalapadum
1 Answers
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You might get inspiration (or just run) Advancing Front Reconstruction
No idea how your point cloud looks, but maybe Point Set Shape Detection might help you to identify flat areas.
