Greedy triangulation

노트

위키데이터

• ID : Q28811699

말뭉치

1. We present a new method for testing compatibility of candidate edges in the greedy triangulation, and new results on the rank of edges in various triangulations.
2. Based on these results, we present fast greedy triangulation algorithms with expected case running time of O(n log n) for uniform distributions over convex regions.
3. We prove that the greedy triangulation heuristic for minimum weight triangulation of convex polygons yields solutions within a constant factor from the optimum.
4. bound on the approximation factor of the Delaunay triangulation heuristic for minimum weight triangulation of convexn-vertex polygons.
5. Finally, we observe that the greedy triangulation for convex polygons having so-called semicircular property can be constructed in timeO(n logn).
6. Since we use more knowledge about the structure of a random point set and its greedy triangulation, our algorithm needs only elementary data structures and simple bucketing techniques.
7. Rather than solve one problem, this is based on propose to Delaunay Triangulation as an alternative for different application on the area.
8. GreedyProjectionTriangulation is an implementation of a greedy triangulation algorithm for 3D points based on local 2D projections.
9. First, the feature information is comprehensively described using FPFH feature description and the local correlation of the feature information is established using greedy projection triangulation.
10. We also prove properties about the expected lengths of edges in greedy and Delaunay triangulations of uniformly distributed points.
11. 1) times the length of a minimum weight triangulation of the polygon (under the assumption that no three vertices lie on the same line).
12. Finally, a simple linear-time algorithm is presented and analyzed for computing greedy triangulations of polygons with the so called semi-circle property.
13. A common approach for finding good decompositions is iteratively executing a greedy triangulation algorithm (e.g. minfill) with randomized tie-breaking.
14. Don't consider points for triangulation if their normal deviates more than this value from the query point's normal.
15. By selecting MethodMethodMethodMethodMethodmethod ='greedy'"greedy""greedy""greedy""greedy""greedy", a so called greedy triangulation algorithm is invoked.
16. The greedy triangulation algorithm starts by initializing a surface with one triangle constructed from three SNC-eligible, neighboring points.
17. neigh_orient_tol <alpha> reports the surface curvature parameter 'alpha' "alpha" "alpha" "alpha" "alpha" "alpha" in degrees that was used for the triangulation.
18. implicit'"implicit""implicit""implicit""implicit""implicit" an implicit triangulation algorithm based on a Poisson solver (see the paper in References) is invoked.
19. Computing the triangulation of a polygon is a fundamental algorithm in computational geometry.
20. The triangulation does not introduce any additional vertices and decomposes the polygon into n-2 triangles.
21. This measure can also be calculated based on two other dimensions of the network: the Minimum Spanning Tree (MST) and the Greedy Triangulation (GT).
22. The Greedy Triangulation (GT) adds missing links between all nodes so as to make it complete (maximal) without breaking its planarity (C).
23. Triangulation is performed locally, by projecting the local neighborhood of a point along the point’s normal, and connecting unconnected points.

메타데이터

위키데이터

• ID : Q28811699

Spacy 패턴 목록

• [{'LOWER': 'greedy'}, {'LEMMA': 'triangulation'}]