We present a variational approach to dense stereo reconstruction which combines powerful tools such as regularization and multi-scale processing to estimate directly depth from a number of stereo images, while preserving depth discontinuities. The problem is set as a regularization and minimization of a nonquadratic functional. The Tikhonov quadratic regularization term usually used to recover smooth solution is replaced by a function of the gradient depth specifically derived to allow depth discontinuities formation in the solution. Conditions to be fulfilled by this specific regularizing term to preserve discontinuities are also presented. To solve this problem in the discrete case, a PDE-based explicit scheme for moving iteratively towards the solution has been developed. This approach presents the additional advantages of not introducing any intermediate representation such as disparity or rectified images: depth is computed directly from the grey-level images and we can also dea...

This paper presents a new intensity-based stereo algorithm using cooperative bidirectional matching with a hierarchical multilevel structure. Based on a new model of piecewise smooth depth fields and the consistency constraint, the algorithm is able to estimate the 3-D structure and detect its discontinuities and the occlusion reliably with low computational costs. In order to find the global optimal estimates, we utilize a multiresolution two-stage algorithm minimizing nonconvex cost functions, which is equivalent to the MAP estimation. This basic framework computing the 3-D structure from binocular stereo images has been extended to the trinocular stereo vision for a further improvement of the performance. A few examples for the binocular and trinocular stereo problems are given to illustrate the performance of the new algorithms.

This paper describes a novel system for computing a three-dimensional (3-D) range segmentation of an arbitrary visible scene using focus information. The process of range segmentation is divided into three steps: an initial range classification, a surface merging process, and a 3-D multiresolution range segmentation. First, range classification is performed to obtain quantized range estimates. The range classification is performed by analyzing focus cues within a Bayesian estimation framework. A combined energy functional measures the degree of focus and the Gibbs distribution of the class field. The range classification provides an initial range segmentation. Second, a statistical merging process is performed to merge the initial surface segments. This gives a range segmentation at a coarse resolution. Third, 3-D multiresolution range segmentation (3-D MRS) is performed to refine the range segmentation into finer resolutions. The proposed range segmentation method does not require initial depth estimates, it allows the analysis of scenes containing multiple objects, and it provides a rich description of the 3-D structure of a scene.

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We describe an edge-based stereo correspondence algorithm in the model-driven vision system. A constraint derived from the 3D model is used to prune false alarms and speed up the matching process. This constraint is based on computional considerations and experimental and psychological observations concerning vertical disparities. An edge constraint is also presented. A numbering scheme is used to facilitate the implementation of the correspondence algorithm using dynamic programming. Experiments on natural images show that the correspondence of an edge can usually be achieved in a few seconds. The computed disparities are further incorporated into object model to refine the estimates of object pose. Keywords: Correspondence, Constraints, Cost function, Model-driven vision, Dynamic programming 1 Introduction The work reported here is a part of an ongoing research project "model-driven stereo vision under variable camera geometry", as shown in Figure 1. Three major stages c...

We present a method for computing dense stereo correspondences in calibrated monocular video by iteratively and stochastically sampling match quality values in the disparity search space. Most existing methods exhaustively compute local correspondence quality before searching for a globally optimal solution. Instead, we iteratively refine a correspondence estimate by perturbing it with random noise and formulating an influence at each sample based on the perturbation and its effect on correspondence match quality. Local influence is aggregated to recover consistent trends in match quality caused by the piecewise-continuous structure of the scene. Correspondence estimates for a given frame pair are seeded with the estimates from the previous frame pair, allowing convergence to occur across multiple frame pairs. Index Terms—Stereo vision, computational geometry, stochastic approximation, recursive estimation, simulated annealing 1.

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 [2] and a cooperative approach by Zitnick and Kanade [27], 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. Index Terms—Computational geometry, cooperative stereo, recursive estimation, simulated annealing, stereo vision, stochastic approximation. I.