Generalized Image Acquisition and Analysis

BlurTags: Spatially Varying PSF Estimation with Out-of-Focus Patterns

In this paper, we consider the problem of animation reconstruction, i.e., the reconstruction of shape and motion of a deformable object from dynamic 3D scanner data, without using user provided template models. Unlike pre- vious work that addressed this problem, we do not rely on locally conver- gent optimization but present a system that can handle fast motion, tem- porally disrupted input, and can correctly match objects that disappear for extended time periods in acquisition holes due to occlusion. Our approach is motivated by cartography: We first estimate a few landmark correspon- dences, which are extended to a dense matching and then used to recon- struct geometry and motion. We propose a number of algorithmic building blocks: a scheme for tracking landmarks in temporally coherent and inco- herent data, an algorithm for robust estimation of dense correspondences under topological noise, and the integration of local matching techniques to refine the result. We describe and evaluate the individual components and propose a complete animation reconstruction pipeline based on these ideas. We evaluate our method on a number of standard benchmark data sets and show that we can obtain correct reconstructions in situations where other techniques fail completely or require additional user guidance such as a template model.

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Intrinsic Shape Matching by Planned Landmark Sampling

Art Tevs, Alexander Berner, Michael Wand, Ivo Ihrke, Hans-Peter Seidel
In: Proceedings of EUROGRAPHICS 2011.



Abstract

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.
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Bibtex

@INPROCEEDINGS{Tevs11:plansac,
AUTHOR = {Art Tevs and Alexander Berner and Michael Wand and Ivo Ihrke and Hans-Peter Seidel},
EDITOR = {Deussen, Oliver and Chen, Min},
TITLE = {{Intrinsic Shape Matching by Planned Landmark Sampling}},
BOOKTITLE = {Computer Graphics Forum (Proc. EUROGRAPHICS)},
ORGANIZATION = {Eurographics},
PADDRESS = {Oxford, UK},
ADDRESS = {Llandudno, UK},
PUBLISHER = {Blackwell},
YEAR = {2011},
PAGES = {543--552},
}
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