A Kaleidoscopic Approach to Surround Geometry and Reflectance Acquisition
We describe a system for acquiring reflectance fields
of objects without moving parts and without a massively
parallel hardware setup. Our system consists of a set of
planar mirrors which serve to multiply a single camera and
a single projector into a multitude of virtual counterparts.
Using this arrangement, we can acquire reflectance fields
with an average angular sampling rate of about 120+
view/light pairs per surface point. The mirror system allows
for freely programmable illumination with full directional
coverage. We employ this setup to realize a 3D acquisition
system that employs structured illumination to capture the
unknown object geometry, in addition to dense reflectance
sampling. On the software side, we combine state-of-the-art
3D reconstruction algorithms with a reflectance sharing
technique based on non-negative matrix factorization
in order to reconstruct a joint model of geometry and
reflectance. We demonstrate for a number of test scenes
that the kaleidoscopic approach can acquire complex
reflectance properties faithfully. The main limitation is
that the multiplexing approach limits the attainable spatial
resolution, trading it off for improved directional coverage.
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},
}