5271425321 Slide01


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5271425321 Slide01

  1. 1. Detection of closed sharp edges in point clouds using normal estimation and graph theory Demarsin, K., Vanderstraeten, D., Volodine, T., and Roose Reviewer : Thanarak Raktham copyright @ 2008
  2. 2. Reverse Engineer Point Cloud Surface Mesh copyright @ 2008
  3. 3. Detection of closed sharp edges in point clouds using normal estimation and graph theory Source Computer-Aided Design Volume 39 , Issue 4 (April 2007) Pages 276-283 Year of Publication: 2007 ISSN:0010-4485 Authors Kris Demarsin ,Denis Vanderstraeten ,Tim Volodine ,Dirk Roose Publisher Butterworth-Heinemann Newton, MA, USA copyright @ 2008
  4. 4. Research Interest The reconstruction of a surface model from a point cloud is an important task in t he reverse engineering of industrial parts. We aim at constructing a curve network on the point cloud that will define the border o f the various surface patches. By using normal estimation and graph theory copyright @ 2008
  5. 5. Sharp feature line extraction algorithm Algorithm 1 High level description of the algorithm. • 1. Segment point cloud using the normals => point clusters (clusters) (Fig. 1) Fig. 1. First order segmentation of two intersecting cylinders. copyright @ 2008
  6. 6. • 2. Build graph Gall connecting neighboring clusters (Figs. 2 and 3(a)) copyright @ 2008
  7. 7. • 3. Add edges, indicating a piece of a sharp feature line, to Gall =>Gextended (Fig. 3(b)) copyright @ 2008
  8. 8. • 4. Build the pruned minimum spanning tree of Gextended => Gpruned_mst (Fig. 3(c)) pruned_ copyright @ 2008
  9. 9. • 5. Prune short branches in Gpruned_mst => GGpruned_branches (Fig. 3(d)) pruned_ Gpruned_ copyright @ 2008
  10. 10. • 6. Close the sharp feature lines in Gpruned_branches =>Gclosed (Fig. 3(e)) pruned_ copyright @ 2008
  11. 11. • 7. Smooth the sharp feature lines in Gclosed => Gsmooth (Fig. 3(f)) copyright @ 2008
  12. 12. Result This paper presented an algorithm to extract sharp edges from a point cloud without estimating the curvatur e and without triangulating the point cloud. Additionally, a ll extracted lines are closed at the end of the algorithm. We start with a very simple region growing method with well chosen normals, resulting in an initial clustering bas ed on the sharp edges. Afterwards, we build and manipul ate a graph of the clusters. Using a graph structure at th e level of clusters yields faster execution times and less memory consumption copyright @ 2008
  13. 13. Future work construct this network, which consists of a set of loops, where each loop defines the boundary of an area where a patch can be fitted. When all these se gments are known, we can continue with each seg ment individually to detect also tangent continuou s but curvature discontinuous features like fillets. copyright @ 2008
  14. 14. copyright @ 2008