Abstract
We present an efficient method that computes dense stereo correspondences by stochastically sampling match quality values. Nonexhaustive sampling facilitates the use of quality metrics that take unique values at noninteger disparities. Depth estimates are iteratively refined with a stochastic cooperative search by perturbing the estimates, sampling match quality, and reweighting and aggregating the perturbations. The approach gains significant efficiencies when applied to video, where initial estimates are seeded using information from the previous pair in a novel application of the Z-buffering algorithm. This significantly reduces the number of search iterations required. We present a quantitative accuracy evaluation wherein the proposed method outperforms a microcanonical annealing approach by Barnard and a cooperative approach by Zitnick and Kanade , while using fewer match quality evaluations than either. The approach is shown to have more attractive memory usage and scaling than alternatives based on exhaustive sampling.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.