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Multiview Stereo via Volumetric Graphcuts and Occlusion Robust PhotoConsistency
, 2007
"... This paper presents a volumetric formulation for the multiview stereo problem which is amenable to a computationally tractable global optimisation using Graphcuts. Our approach is to seek the optimal partitioning of 3D space into two regions labelled as ‘object’ and ‘empty’ under a cost functional ..."
Abstract

Cited by 187 (9 self)
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This paper presents a volumetric formulation for the multiview stereo problem which is amenable to a computationally tractable global optimisation using Graphcuts. Our approach is to seek the optimal partitioning of 3D space into two regions labelled as ‘object’ and ‘empty’ under a cost functional consisting of the following two terms: (1) A term that forces the boundary between the two regions to pass through photoconsistent locations and (2) a ballooning term that inflates the ‘object ’ region. To take account of the effect of occlusion on the first term we use an occlusion robust photoconsistency metric based on Normalised Cross Correlation, which does not assume any geometric knowledge about the reconstructed object. The globally optimal 3D partitioning can be obtained as the minimum cut solution of a weighted graph.
Cluster Sampling and its Applications to Segmentation, Stereo and Motion
, 2005
"... Many computer vision problems can be formulated as graph partition problems that minimize energy functions. Generally applicable algorithms like the Gibbs sampler can perform the minimization task, but they are very slow to converge, especially since the graphs in vision tasks are large (10 10 nod ..."
Abstract

Cited by 1 (1 self)
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Many computer vision problems can be formulated as graph partition problems that minimize energy functions. Generally applicable algorithms like the Gibbs sampler can perform the minimization task, but they are very slow to converge, especially since the graphs in vision tasks are large (10 10 nodes). On the other hand, computationally e#ective algorithms like Graph Cuts and Belief Propagation are specialized to particular forms of energy functions, and they cannot be applied for complex statistical models using generative models and highorder priors. In this thesis, a new stochastic algorithm capable of sampling arbitrary energy functions defined on graph partitions is presented. To increase e#ciency, the algorithm uses the image information to make informed jumps in the search space. The image information is given in the form of edge weights and represents an empirical probability that the nodes connected by the edge belong to the same object. At each step, the algorithm creates clusters of nodes by turning on/o# the edges randomly according to their weights, and changes the label of all nodes in one cluster (connected component) in a single move. Each move is accepted or rejected according to an acceptance probability given by a simple and explicit xvii equation. The algorithm is applied to 4 important problems in computer vision: image segmentation, perceptual organization, stereo matching and motion segmentation. To address di#erent computational or representational issues, multigrid, multilevel and multicue variants of the algorithm are presented. In image segmentation, the algor...
Multiview Stereo via Volumetric Graphcuts
"... This paper presents a novel formulation for the multiview scene reconstruction problem. While this formulation benefits from a volumetric scene representation, it is amenable to a computationally tractable global optimisation using Graphcuts. The algorithm proposed uses the visual hull of the scene ..."
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This paper presents a novel formulation for the multiview scene reconstruction problem. While this formulation benefits from a volumetric scene representation, it is amenable to a computationally tractable global optimisation using Graphcuts. The algorithm proposed uses the visual hull of the scene to infer occlusions and as a constraint on the topology of the scene. A photo consistencybased surface cost functional is defined and discretised with a weighted graph. The optimal surface under this discretised functional is obtained as the minimum cut solution of the weighted graph. Our method provides a viewpoint independent surface regularisation, approximate handling of occlusions and a tractable optimisation scheme. Promising experimental results on real scenes as well as a quantitative evaluation on a synthetic scene are presented. 1