Generalized Image Acquisition and Analysis

Discovering the Structure of a Planar Mirror System from Multiple Observations of a Single Point

We investigate the problem of identifying the position of a viewer inside a room of planar mirrors with unknown geometry in conjunction with the room’s shape parameters. We consider the observations to consist of angularly resolved depth measurements of a single scene point that is being observed via many multi-bounce interactions with the specular room geometry. Applications of this problem statement include areas such as calibration, acoustic echo cancelation and time-of-flight imaging. We theoretically analyze the problem and derive sufficient conditions for a combination of convex room geometry, observer, and scene point to be reconstructable. The resulting constructive algorithm is exponential in nature and, therefore, not directly applicable to practical scenarios. To counter the situation, we propose theoretically devised geo- metric constraints that enable an efficient pruning of the solution space and develop a heuristic randomized search algorithm that uses these constraints to obtain an effective solution. We demon- strate the effectiveness of our algorithm on extensive simulations as well as in a challenging real-world calibration scenario.


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

Alexander Reuter, Hans-Peter Seidel, Ivo Ihrke
In: WSCG Communication Papers, 2012


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.


author = {Alexander Reuter and Hans-Peter Seidel and Ivo Ihrke},
title = {BlurTags: spatially varying PSF estimation with out-of-focus patterns},
booktitle = {Proceedings of WSCG (Communication Papers)},
year = 2012,
pages = "xx--yy",
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