A revisit to cost aggregation in stereo matching: How far can we reduce its computational redundancy?

Min, Dongbo; Lu, Jiangbo; Minh, N.

doi:10.1109/iccv.2011.6126416

Cited by 88 publications

(50 citation statements)

References 11 publications

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“…Therefore, those methods are limited in that they do not scale to extremely large label sets. To overcome this problem, with regard to stereo matching, Min et al [26,27] proposed a technique to estimate a compact disparity subset for every pixel by considering disparities with the local minima of the pre-filtered cost values. Although this method efficiently achieves high-quality results with the Middlebury stereo benchmark [25], it cannot be applied to general multi-labeling problems directly.…”

Section: Cost Aggregation Methods For Labeling Problemsmentioning

confidence: 99%

Efficiency-enhanced cost-volume filtering featuring coarse-to-fine strategy

Furuta

Ikehata

Yamaskai

et al. 2017

Multimed Tools Appl

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Cost-volume filtering (CVF) is one of the most widely used techniques for solving general multi-labeling problems based on a Markov random field (MRF). However it is inefficient when the label space size (i.e., the number of labels) is large. This paper presents a coarse-to-fine strategy for cost-volume filtering that efficiently and accurately addresses multi-labeling problems with a large label space size. Based on the observation that true labels at the same coordinates in images of different scales are highly correlated, we truncate unimportant labels for cost-volume filtering by leveraging the labeling output of lower scales. Experimental results show that our algorithm achieves much higher efficiency than the original CVF method while maintaining a comparable level of accuracy. Although we performed experiments that deal with only stereo matching and optical flow estimation, the proposed method can be employed in many other applications because of the applicability of CVF to general discrete pixel-labeling problems based on an MRF.

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Section: Cost Aggregation Methods For Labeling Problemsmentioning

confidence: 99%

Efficiency-enhanced cost-volume filtering featuring coarse-to-fine strategy

Furuta

Ikehata

Yamaskai

et al. 2017

Multimed Tools Appl

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show abstract

“…Min et al [20] reduced the search range using a subset of informative disparity hypotheses. However, this method cannot get precise results at depth discontinuities as the aggregation windows located on depth edges represent pixels from different depths.…”

Section: : Refines Disparity B : Aggregates Cost a : Computes Matchmentioning

confidence: 99%

Visual stereo matching combined with intuitive transition of pixel values

Shin

Kim

Chung

2015

Multimed Tools Appl

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The objective of stereo matching is to find the corresponding pixels from similar two or more images. However, it is difficult problem to get precise and consistent disparity under a variety of real world situations. In other words, the color values of stereo images are easily influenced by radiometric factors such as illumination direction, illumination color, and camera exposure. Therefore, conventional stereo matching methods can have low performances under radiometric conditions. In this paper, we propose a novel stereo matching approach that is robust in controlling various radiometric variations such as local and global radiometric variations. We designed a hybrid stereo matching approach using transition of pixel values and data fitting. Transition of pixel values is utilized for the coarse stereo matching stage, and polynomial curve fitting is used for the fine stereo matching stage. Experimental results show that the proposed method has better performances compared to the stereo matching algorithms of comparison group under severely different radiometric conditions between stereo images. Consequentially, we demonstrate that the proposed method is less sensitive to various radiometric variations, and shows an outstanding performance in computational complexity.

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“…Note that Equation 2 becomes the multi-window method [5], the adaptive window method [6], and the adaptive weight method [7] according to the weight function w(x, y) and/or the neighborhood N . Thus, in conventional methods, the number of iteration should be specified in advance and it significantly influences the performance of algorithms [7,9,10,11,12]. Recently, the RWR has become increasingly popular, since its restarting term gives the meaningful information in a steady-state, allowing it to consider the global relation at all scales [19].…”

Section: Problem Statementmentioning

confidence: 99%

“…Recently, De-Maeztu and Villanueva presented a diffusion-based correspondence matching in which the computed weights as well as the costs were diffused so as to lower the computational cost [13]. While most methods have concentrated on reducing the image resolution and on modifying the window size and/or the shape, Min et al accelerated the matching speed by compressing a search range space and sampling points in the matching window [11].…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic Correspondence Matching using Random Walk with Restart

Oh¹,

Ham²,

Sohn³

2012

Procedings of the British Machine Vision Conference 2012

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This paper presents a probabilistic method for correspondence matching with a framework of the random walk with restart (RWR). The matching cost is reformulated as a corresponding probability, which enables the RWR to be utilized for matching the correspondences. There are mainly two advantages in our method. First, the proposed method guarantees the non-trivial steady-state solution of a given initial matching probability due to the restarting term in the RWR. It means the number of iteration, a crucial parameter which influences the performance of algorithm, is not needed in contrast to the conventional methods. This gives the consistent results regardless of the evolution time. Second, only an adjacent neighborhood is considered when the matching probabilities are inferred, which lowers the computational complexity while not sacrificing performance. Experimental results show that the performance of the proposed method is competitive to that of state-of-the-art methods both qualitatively and quantitatively.

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A revisit to cost aggregation in stereo matching: How far can we reduce its computational redundancy?

Cited by 88 publications

References 11 publications

Efficiency-enhanced cost-volume filtering featuring coarse-to-fine strategy

Efficiency-enhanced cost-volume filtering featuring coarse-to-fine strategy

Visual stereo matching combined with intuitive transition of pixel values

Probabilistic Correspondence Matching using Random Walk with Restart

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