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

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 geometric 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 demonstrate the effectiveness of our algorithm on extensive simulationsas well as in a challenging real-world calibration scenario.
Document type :
Conference papers
Complete list of metadatas

Cited literature [23 references]  Display  Hide  Download

https://hal.inria.fr/hal-00863919
Contributor : Xavier Granier <>
Submitted on : Thursday, September 19, 2013 - 9:01:49 PM
Last modification on : Thursday, September 20, 2018 - 7:54:02 AM
Long-term archiving on : Friday, April 7, 2017 - 12:20:33 AM

File

cvpr13.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-00863919, version 1

Citation

Ilya Reshtouski, Alkhazur Manakov, Ayush Bhandari, Ramesh Raskar, Hans-Peter Seidel, et al.. Discovering the Structure of a Planar Mirror System from Multiple Observations of a Single Point. CVPR - Conference on Computer Vision and Pattern Recognition, Jun 2013, Portland, Oregon, United States. ⟨hal-00863919⟩

Share

Metrics

Record views

500

Files downloads

222