This paper deals with a compact catadioptric omnidirectional stereovision system based on a single camera and multi-mirrors (at least two mirrors). Many configurations were empirically designed in previous works with the aim to obtain a good 3D reconstruction accuracy. In this paper, we propose to use optimization techniques for omnidirectional catadioptric stereovision design, by using a stochastic local search method in order to find a good sensor (number, relative positions and sizes of mirrors). We explain principles of our approach and provide automatically designed sensors with a number of mirrors from two to nine. We finally simulate the 3D-reconstruction of a real environment modeled under a ray-tracing software with some of these sensors. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Dequen, G., Devendeville, L., & Mouaddib, E. (2007). Stochastic local search for omnidirectional catadioptric stereovision design. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4478 LNCS, pp. 404–411). Springer Verlag. https://doi.org/10.1007/978-3-540-72849-8_51
Mendeley helps you to discover research relevant for your work.