Generalized Image Acquisition and Analysis

From Capture to Simulation - Connecting Forward and Inverse Problems in Fluids

We explore the connection between fluid capture, simulation and proximal methods, a class of algorithms commonly used for inverse problems in image processing and computer vision. Our key finding is that the proximal operator constraining fluid velocities to be divergence-free is directly equivalent to the pressure-projection methods commonly used in incompressible flow solvers. This observation lets us treat the inverse problem of fluid tracking as a constrained flow problem all while working in an efficient, modular framework. In addition it lets us tightly couple fluid simulation into flow tracking, providing a global prior that significantly increases tracking accuracy and temporal coherence as compared to previous techniques. We demonstrate how we can use these improved results for a variety of applications, such as re-simulation, detail enhancement, and domain modification. We furthermore give an outlook of the applications beyond fluid tracking that our proximal operator framework could enable by exploring the connection of deblurring and fluid guiding.


Intrinsic Shape Matching by Planned Landmark Sampling

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


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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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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