On building an accurate stereo matching system on graphics hardware

Mei, Xing; Sun, Xingming; Zhou, Min; Jiao, Shaohui; Wang, Haitao; Zhang, Xiaopeng

doi:10.1109/iccvw.2011.6130280

Cited by 476 publications

(385 citation statements)

References 17 publications

Supporting

Mentioning

381

Contrasting

Unclassified

Order By: Relevance

“…It uses the Census transform [22] and currently provides the highest quality disparity results compared to real-time hardware implementations in ASICs and FPGAs. The hardware presented in [15] uses low complexity Mini-Census method to determine the matching cost, and aggregates the Hamming costs following the method in [12]. Due to high complexity cost aggregation, the hardware proposed in [15] requires high memory bandwidth and intense hardware Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/vlsi resource utilization, even for low resolution (LR) video.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, due to a 1-bit representation of neighboring pixels, the Census easily selects wrong disparity results. In order to correct these mismatches, our proposed AWDE algorithm uses the support of the BW-SAD, instead of using the complex cost aggregation method [12,15].…”

Section: Introductionmentioning

confidence: 99%

“…These are ranked with respect to their performance in the evaluation of Middlebury benchmarks [11]. Although top-performer algorithms provide impressive visual and quantitative results [12][13][14], their implementations in real-time High Resolution (HR) stereo video are challenging due to their complex multi-step refinement processes or their global processing requirements that demand huge memory size and bandwidth. For example, the AD-Census algorithm [12], currently the top published performer, provides successful results that are very close to the ground truths.…”

Section: Introductionmentioning

confidence: 99%

“…Although top-performer algorithms provide impressive visual and quantitative results [12][13][14], their implementations in real-time High Resolution (HR) stereo video are challenging due to their complex multi-step refinement processes or their global processing requirements that demand huge memory size and bandwidth. For example, the AD-Census algorithm [12], currently the top published performer, provides successful results that are very close to the ground truths. However, this algorithm consists of multi disparity enhancement subalgorithms, and implementing them into a mid-range FPGA is very challenging both in terms of hardware resource and memory limitations.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Dynamically adaptive real-time disparity estimation hardware using iterative refinement

et al. 2014

View full text Add to dashboard Cite

a b s t r a c tThe computational complexity of disparity estimation algorithms and the need of large size and bandwidth for the external and internal memory make the real-time processing of disparity estimation challenging, especially for High Resolution (HR) images. This paper proposes a hardware-oriented adaptive window size disparity estimation (AWDE) algorithm and its real-time reconfigurable hardware implementation that targets HR video with high quality disparity results. Moreover, an enhanced version of the AWDE implementation that uses iterative refinement (AWDE-IR) is presented. The AWDE and AWDE-IR algorithms dynamically adapt the window size considering the local texture of the image to increase the disparity estimation quality. The proposed reconfigurable hardware architectures of the AWDE and AWDE-IR algorithms enable handling 60 frames per second on a Virtex-5 FPGA at a 1024 Â 768 XGA video resolution for a 128 pixel disparity range.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamically adaptive real-time disparity estimation hardware using iterative refinement

et al. 2014

View full text Add to dashboard Cite

show abstract

“…To get higher-accuracy disparity results, cross-based aggregation methods [2], [3] were proposed to form a shapeadaptive support region for each pixel. This method helped improve the performance on depth discontinuities and textureless regions.…”

Section: Introductionmentioning

confidence: 99%

Stereo Matching Based on Efficient Image-Guided Cost Aggregation

Zhan

Zhang

et al. 2016

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYCost aggregation is one of the most important steps in local stereo matching, while it is difficult to fulfill both accuracy and speed. In this letter, a novel cost aggregation, consisting of guidance image, fast aggregation function and simplified scan-line optimization, is developed. Experiments demonstrate that the proposed algorithm has competitive performance compared with the state-of-art aggregation methods on 32 Middlebury stereo datasets in both accuracy and speed.

show abstract

Stereo Matching with Multi‐scale Based on Anisotropic Match Cost

Liu

Cai

Zhou

et al. 2020

Concurrency and Computation

View full text Add to dashboard Cite

Binocular vision is a popular method for 3d measurement, but the precision and efficiency is still a limitation. In order to solve the problem of low precision and high mismatching rate of current stereo match methods in weak texture region, a local stereo matching algorithm with multi-scale based on anisotropic match cost was proposed. Not only including the gray value between the adjacent pixels as the absolute difference (AD) method, the matching cost function also involved gradient and phase information to make an anisotropic evaluation and eliminate the outliers of the flat area. Meanwhile, a multi-scale strategy with the image pyramid was referenced, and the dynamic disparity search range of variable window was used on the original cost aggregation framework by improving the stereo matching algorithm of cross-scale cost aggregation. The matching cost volume in each scale space used the dynamic support window to guide the filter to aggregate the matching cost. Finally, to overcome the problems of disparity selection ambiguity of WTA (winner-take-all) strategy and the horizontal fringe introduced by left-right consistency (LRC) detection, the weighted median filter based on guided filter weight was used to carry out disparity refinement. According to the experiments, the method got more high matching accuracy, and the average error on the Middlebury test platform reached 5.25%.

show abstract

On building an accurate stereo matching system on graphics hardware

Cited by 476 publications

References 17 publications

Dynamically adaptive real-time disparity estimation hardware using iterative refinement

Dynamically adaptive real-time disparity estimation hardware using iterative refinement

Stereo Matching Based on Efficient Image-Guided Cost Aggregation

Stereo Matching with Multi‐scale Based on Anisotropic Match Cost

Contact Info

Product

Resources

About