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.
Projects
Discovering the Structure of a Planar Mirror System from Multiple Observations of a Single Point
CVPR 2013
Abstract
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.
Bibtex
@inproceedings{Reshetouski:13,
author = {Ilya Rehsetouski and Alkhazur Manakov and Ayush Bhandari and Ramesh Raskar and Hans-Peter Seidel and Ivo Ihrke},
title = {Discovering the Structure of a Planar Mirror System from Multiple Observations of a Single Point},
booktitle = {Proceedings of CVPR},
year = 2013,
pages = {xx--yy},
}
author = {Ilya Rehsetouski and Alkhazur Manakov and Ayush Bhandari and Ramesh Raskar and Hans-Peter Seidel and Ivo Ihrke},
title = {Discovering the Structure of a Planar Mirror System from Multiple Observations of a Single Point},
booktitle = {Proceedings of CVPR},
year = 2013,
pages = {xx--yy},
}