2007_autumn_mster_liuyu2007_autumn_master_liuyu2007_autumn_master_liuyu2007_autumn_master_liuyu.ppt

Detection of closed sharp edges in point clouds Speaker: Liuyu Time:2007-09-27 Background Knowledge Realistic parts to point clouds: Point clouds to “realistic” parts: an important part in the reverse engineering of industrial parts Reference Detection of closed sharp edges in point clouds using normal estimation and graph theory Authors : Kris Demarsin, Denis Vanderstraeten, Tim Volodine, Dirk Roose Publish: CAD 39(2007) 276-283 Authors Kris Demarsin: K.U.Leuven, Belgium Department of Computer Science Promoter: D. Roose Co-Promoter: D. Vanderstaeten (Metris)  Authors Tim Volodine research assistant Department of Computer Science Celestijnenlaan 200A 1.28 B-3001 Heverlee Promoter: D. Roose Co-Promoter: D. Vanderstaeten (Metris)  Authors Dirk Roose : Professor at the department of Computer Science, Faculty of Applied Sciences, K.U.Leuven; Head of the research group Scientific Computing Email: Dirk.Roose@cs.kuleuven.ac.be Algorithm 1 1. Segment point cloud using the normals＝ point clusters(clusters); 2. Build graph Gall connecting neighboring clusters; 3. Add edges, indicating a piece of a sharp feature line, to Gall ＝ Gextended; 4. Build the pruned minimum spanning tree of Gextended ＝ Gextended; Algorithm 1 5. Prune short branches in Gpruned_mst = Gpruned_branches ; 6. Close the sharp feature lines in Gpruned_branches = Gclosed ; 7. Smooth the sharp feature lines in Gclosed = Gsmooth . Step 1 . Clusters Delaunay neighorhood of p Choose k(=20) nearest neighbors of p: The least square plane through the (k+1) points Step 1 . Clusters Project the points onto the plane Delaunay triangulate the projected points, Obtaining the Delaunay neighorhood of p Step 1. Clusters Normal estimation and First order segmentation of point cloud N(p) : the normal in p is estimated as the normal of the least squares plane through Delaunay neighhood of p p1, p2 are two ad