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Intrinsic Shape Matching by Planned Landmark Sampling
Author(s) -
Tevs A.,
Berner A.,
Wand M.,
Ihrke I.,
Seidel H.P.
Publication year - 2011
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/j.1467-8659.2011.01879.x
Subject(s) - geodesic , landmark , matching (statistics) , generality , computer science , benchmark (surveying) , sampling (signal processing) , degrees of freedom (physics and chemistry) , algorithm , mathematical optimization , topology (electrical circuits) , mathematics , artificial intelligence , computer vision , geometry , statistics , psychology , physics , geodesy , filter (signal processing) , quantum mechanics , combinatorics , psychotherapist , geography
Recently, the problem of intrinsic shape matching has received a lot of attention. A number of algorithms have been proposed, among which random‐sampling‐based techniques have been particularly successful due to their generality and efficiency. We introduce a new sampling‐based shape matching algorithm that uses a planning step to find optimized “landmark” points. These points are matched first in order to maximize the information gained and thus minimize the sampling costs. Our approach makes three main contributions: First, the new technique leads to a significant improvement in performance, which we demonstrate on a number of benchmark scenarios. Second, our technique does not require any keypoint detection. This is often a significant limitation for models that do not show sufficient surface features. Third, we examine the actual numerical degrees of freedom of the matching problem for a given piece of geometry. In contrast to previous results, our estimates take into account unprecise geodesics and potentially numerically unfavorable geometry of general topology, giving a more realistic complexity estimate.