Dense Disparity Real-Time Stereo Vision Algorithm for Resource-Limited Systems

Tippetts, Beau; Lee, Dah-Jye; Archibald, James K.; Lillywhite, Kirt

doi:10.1109/tcsvt.2011.2163444

Cited by 39 publications

(30 citation statements)

References 20 publications

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“…Window based algorithms have higher processing speed because of lower data bandwidth and less computation complexity, but matching errors are higher. For the same algorithm, generally, the processing speed, quality and power consumption of FPGA or ASIC can outperform GPU accelerator generally, as proved by [80][81][82][83][84][85][86][87][88][89][90][91]. The disadvantages of FPGA or ASIC are longer developing time and less processing flexibility compared with GPU.…”

Section: Software and Hardware Processing Methods Comparisonmentioning

confidence: 99%

Software and Hardware Implementations of Stereo Matching

Zhou¹,

Sun²,

Zhan³

et al. 2014

IJSIP

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Section: Software and Hardware Processing Methods Comparisonmentioning

confidence: 99%

Software and Hardware Implementations of Stereo Matching

Zhou¹,

Sun²,

Zhan³

et al. 2014

IJSIP

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show abstract

“…There are some similarities in cost matching function through the use of window-based techniques. They are the sum of absolute differences (SAD) (Tippetts et al, 2011), the sum of squared differences (SSD; Yang and Pollefeys, 2003) and the normalized cross correlation (NCC; Satoh, 2011). Observed from all of these works, the main problem of window based technique is that it normally assumes that the pixels within the support region have the same disparity.…”

Section: Related Work On Stereo Matching Algorithmsmentioning

confidence: 99%

Stereo Matching Algorithm Based on Illumination Control to Improve the Accuracy

2016

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This paper presents a new method of pixel based stereo matching algorithm using illumination control. The state of the art algorithm for absolute difference (AD) works fast, but only precise at low texture areas. Besides, it is sensitive to radiometric distortions (i.e., contrast or brightness) and discontinuity areas. To overcome the problem, this paper proposes an algorithm that utilizes an illumination control to enhance the image quality of absolute difference (AD) matching. Thus, pixel intensities at this step are more consistent, especially at the object boundaries. Then, the gradient difference value is added to empower the reduction of the radiometric errors. The gradient characteristics are known for its robustness with regard to the radiometric errors. The experimental results demonstrate that the proposed algorithm performs much better when using a standard benchmarking dataset from the Middlebury Stereo Vision dataset. The main contribution of this work is a reduction of discontinuity errors that leads to a significant enhancement on matching quality and accuracy of disparity maps.

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“…Consequently, these methods are typically used to reconstruct highly accurate 3-D model [7], [8]. In contrast, the stereo matching methods estimate disparity maps using multiple images taken by two or more cameras [9], [10]. A number of algorithms have been proposed by using cost aggregation methods [11]- [13] and global optimization techniques [14].…”

Section: Introductionmentioning

confidence: 99%

Reliability-Based Multiview Depth Enhancement Considering Interview Coherence

Choi

Min

Sohn

2014

IEEE Trans. Circuits Syst. Video Technol.

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Color-plus-depth video format has been increasingly popular in 3-D video applications, such as auto-stereoscopic 3-D TV and freeview TV. The performance of these applications is heavily dependent on the quality of depth maps since intermediate views are synthesized using the corresponding depth maps. This paper presents a novel framework for obtaining high-quality multiview color-plus-depth video using a hybrid sensor, which consists of multiple color cameras and depth sensors. Given multiple high-resolution color images and low quality depth maps obtained from the color cameras and depth sensors, we improve the quality of the depth map corresponding to each color view by increasing its spatial resolution and enforcing interview coherence. Specifically, a new up-sampling method considering the interview coherence is proposed to enhance multiview depth maps. This approach can improve the performance of the existing up-sampling algorithms, such as joint bilateral up-sampling and weighted mode filtering, which have been developed to enhance a singleview depth map only. In addition, an adaptive approach of fusing multiple input low-resolution depth maps is proposed based on the reliability that considers camera geometry and depth validity. The proposed framework can be extended into the temporal domain for temporally consistent depth maps. Experimental results demonstrate that the proposed method provides better multiview depth quality than the conventional single-view-based methods. We also show that it provides comparable results, yet much more efficiently, to other fusion approaches that employ both depth sensors and stereo matching algorithm together. Moreover, it is shown that the proposed method significantly reduces bit rates required to compress the multiview color-plus-depth video.Index Terms-Color camera, depth sensor, depth up-sampling, interview coherence, reliability.

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Dense Disparity Real-Time Stereo Vision Algorithm for Resource-Limited Systems

Cited by 39 publications

References 20 publications

Software and Hardware Implementations of Stereo Matching

Software and Hardware Implementations of Stereo Matching

Stereo Matching Algorithm Based on Illumination Control to Improve the Accuracy

Reliability-Based Multiview Depth Enhancement Considering Interview Coherence

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