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Dense Disparity Estimation From Stereo Images

Abstract : Binocular stereovision is based on the process of obtaining the depth information from a pair of left and right views of a scene. In this paper, we describe a new approach for computing a dense disparity field based on a convex set theoretic formulation. The stereo matching problem is solved through the minimization of an objective function under various convex constraints arising from prior knowledge. In order to preserve the discontinuities in the disparity field while getting a stable solution, we consider different appropriate regularization constraints. The resulting multi-constrained optimization problem is solved via an efficient algorithm which was recently introduced in the convex optimization literature. Experimental results on standard data sets demonstrate the validity of the proposed approach.
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Submitted on : Monday, May 29, 2006 - 11:40:00 AM
Last modification on : Friday, February 4, 2022 - 3:11:22 AM
Long-term archiving on: : Wednesday, March 29, 2017 - 12:20:16 PM


  • HAL Id : inria-00069063, version 2


Wided Miled, Jean-Christophe Pesquet, Michel Null Parent. Dense Disparity Estimation From Stereo Images. 3rd International Symposium on Image/Video Communications, Sep 2006, Hammamet, Tunisia. ⟨inria-00069063v2⟩



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