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.
Projects
BlurTags: Spatially Varying PSF Estimation with Out-of-Focus Patterns
In: WSCG Communication Papers, 2012
Abstract
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.
Bibtex
@InProceedings{Reuter12,
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",
}
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",
}