Generalized Image Acquisition and Analysis

Intrinsic Shape Matching by Planned Landmark Sampling

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.

Teaching

winter term 2014/15

Lectures

winter term 2013/14

Advanced Display Technology
Ivo Ihrke, Pascal Picart

winter term 2012/13

Seminars

Parallel Visual Computing
Ivo Ihrke, Tobias Ritschel, Mario Fritz

summer term 2012

Lectures

Computational Photography
Ivo Ihrke
Universität des Saarlandes


winter term 2011/12

Seminars

summer term 2011

Lectures

Computational Photography
Ivo Ihrke
Universität des Saarlandes / JKU Linz
Hispos

winter term 2010/11

Seminars

Research Topics in Computational Photography (project-based seminar)
Ivo Ihrke

summer term 2010

Lectures

Computational Photography
Ivo Ihrke, Matthias Hullin
Universität des Saarlandes

winter term 2009/10

Seminars

Computational Photography and Videography
Ivo Ihrke, Christian Theobalt



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