Real-time stereo on GPGPU using progressive multi-resolution adaptive windows

Zhao, Yong; Taubin, Gabriel

doi:10.1016/j.imavis.2011.01.007

Cited by 20 publications

(15 citation statements)

References 35 publications

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“…We can classify them into two types: the coarse-to-fine strategies where the cost aggregation results from all resolution are merged in order to obtain more accurate results such as [33,42,49], and ones where the results of lower resolution are propagated to higher resolution in order to reduce the search range of labels such as [43,50]. We focus on the latter because our method is classified into latter group.…”

Section: Coarse-to-fine Strategymentioning

confidence: 99%

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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: Coarse-to-fine Strategymentioning

confidence: 99%

“…Zhao et al [50] employed a coarse-to-fine strategy in their elegant implementation on GPGPU for real-time stereo. They limit the search range within ±2 pixels of the disparity value obtained in lower resolution.…”

Section: Coarse-to-fine Strategymentioning

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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“…They report an impressive 140x speed up compared to the CPU implementation. Another GPU stereo matching algorithm using adaptive windows can be found in [13].…”

Section: Gpgpu Implementation Of a Geneticmentioning

confidence: 99%

GPGPU Implementation of a Genetic Algorithm for Stereo Refinement

Arranz¹,

Alvar²

2015

IJIMAI

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-During the last decade, the general-purpose computing on graphics processing units Graphics (GPGPU) has turned out to be a useful tool for speeding up many scientific calculations. Computer vision is known to be one of the fields with more penetration of these new techniques. This paper explores the advantages of using GPGPU implementation to speedup a genetic algorithm used for stereo refinement.The main contribution of this paper is analyzing which genetic operators take advantage of a parallel approach and the description of an efficient state-of-the-art implementation for each one. As a result, speed-ups close to x80 can be achieved, demonstrating to be the only way of achieving close to real-time performance.

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“…Ideally, regarding the uniqueness assumption the difference between both values has to be zero (Zhao & Taubin (2011)). …”

Section: Left Right Consistency Checkmentioning

confidence: 99%

“…The overall rate of points with a higher difference to the ground truth than one pixel is 15.8 %. The approach of Zhao & Taubin (2011) reaches 36 Hz with an algorithm optimized to measure moving parts in stereo images. After using a foreground detection a multi-resolution stereo matching is applied.…”

Section: Comparison With the State Of The Art Of Stereo Image Analysimentioning

confidence: 99%