Real-time Local Stereo Matching Using Edge Sensitive AdaptiveWindows

Dumont, Maarten; Goorts, Patrik; Maesen, Steven; Bekaert, Philippe; Lafruit, Gauthier

doi:10.5220/0005065301170126

Cited by 2 publications

(5 citation statements)

References 13 publications

(16 reference statements)

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“…They will be used both during the initial disparity map estimation in Sect. 4 and during the iterative disparity refinement in Sect. 5.…”

Section: Edge-sensitive Local Support Windowsmentioning

confidence: 99%

“…It is presented in Sect. 4 and covers the other three stages previously mentioned: cost calculation (Sect. 4.1), cost aggregation (Sect.…”

Section: Introductionmentioning

confidence: 99%

“…This time the novelty lies in the cost aggregation stage, where we combine the edge-sensitive local support windows from Sect. 3 into a global support window for increased disparity hypothesis confidence [4].…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Edge-Sensitive Local Stereo Matching with Iterative Disparity Refinement

Dumont

Goorts

Maesen

et al. 2015

E-Business and Telecommunications

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Abstract. First, we present a novel cost aggregation method for stereo matching that uses two edge-sensitive shape-adaptive support windows per pixel region; one following the horizontal edges in the image, the other the vertical edges. Their combination defines the final aggregation window shape that closely follows all object edges and thereby achieves increased hypothesis confidence. Second, we present a novel iterative disparity refinement process and apply it to the initially estimated disparity map. The process consists of four rigorously defined and lightweight modules that can be iterated multiple times: a disparity cross check, bitwise fast voting, invalid disparity handling, and median filtering. We demonstrate that our iterative refinement has a large effect on the overall quality, resulting in smooth disparity maps with sharp object edges, especially around occluded areas. It can be applied to any stereo matching algorithm and tends to converge to a final solution. Finally, we perform a quantitative evaluation on various Middlebury datasets, showing an increase in quality of over several dB PSNR compared with their ground truth. Our whole disparity estimation algorithm supports efficient GPU implementation to facilitate scalability and real-time performance.

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“…They will be used both during the initial disparity map estimation in Sect. 4 and during the iterative disparity refinement in Sect. 5.…”

Section: Edge-sensitive Local Support Windowsmentioning

confidence: 99%

“…It is presented in Sect. 4 and covers the other three stages previously mentioned: cost calculation (Sect. 4.1), cost aggregation (Sect.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Real-Time Edge-Sensitive Local Stereo Matching with Iterative Disparity Refinement

Dumont

Goorts

Maesen

et al. 2015

E-Business and Telecommunications

Self Cite

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show abstract

“…In this section, our goal is to first estimate an initial disparity map [2] that will serve as input to our iterative refinement process in section 4. First, we consider each disparity and calculate (in section 3.1) for each pixel in the left image the difference (i.e.…”

Section: Initial Disparity Estimationmentioning

confidence: 99%

“…We therefore use our previously developed approach to estimate an initial disparity map [2]. It is summarized in section 3 and covers the other three stages previously mentioned: cost calculation, cost aggregation (which relies again on the support windows from section 2), and disparity selection.…”

Section: Introductionmentioning

confidence: 99%

Iterative refinement for real-time local stereo matching

Dumont

Goorts

Maesen

et al. 2014

2014 International Conference on 3D Imaging (IC3D)

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We present a novel iterative refinement process to apply to any stereo matching algorithm. The quality of its disparity map output is increased using four rigorously defined refinement modules, which can be iterated multiple times: a disparity cross check, bitwise fast voting, invalid disparity handling, and median filtering. We apply our refinement process to our recently developed aggregation window method for stereo matching that combines two adaptive windows per pixel region [2]; one following the horizontal edges in the image, the other the vertical edges. Their combination defines the final aggregation window shape that closely follows all object edges and thereby achieves increased hypothesis confidence. We demonstrate that the iterative disparity refinement has a large effect on the overall quality, especially around occluded areas, and tends to converge to a final solution. We perform a quantitative evaluation on various Middlebury datasets. Our whole disparity estimation process supports efficient GPU implementation to facilitate scalability and real-time performance.

show abstract

Real-time Local Stereo Matching Using Edge Sensitive AdaptiveWindows

Cited by 2 publications

References 13 publications

Real-Time Edge-Sensitive Local Stereo Matching with Iterative Disparity Refinement

Real-Time Edge-Sensitive Local Stereo Matching with Iterative Disparity Refinement

Iterative refinement for real-time local stereo matching

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