Real-Time Stereo Vision System: A Multi-Block Matching on GPU

Chang, Qiong; Matsumoto, Tsutomu

doi:10.1109/access.2018.2859445

Cited by 32 publications

(17 citation statements)

References 44 publications

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“…The SVS obtains 3-D information from two images captured from two different cameras separated by a known distance. Similar design of SVS can be found in the literature, 27 –29 The developed computer program for 3-D point localization using SVS can be divided into five steps: images capture, camera calibration, pattern match, computing pixel coordinates to angles, and triangulation. Figure 4 shows the localization of a 3-D point in the scene using the developed SVS.…”

Section: Svs Implementationmentioning

confidence: 99%

Improve three-dimensional point localization accuracy in stereo vision systems using a novel camera calibration method

Ramírez-Hernández

Rodríguez-Quiñonez

Castro-Toscano

et al. 2020

International Journal of Advanced Robotic Systems

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Computer vision systems have demonstrated to be useful in applications of autonomous navigation, especially with the use of stereo vision systems for the three-dimensional mapping of the environment. This article presents a novel camera calibration method to improve the accuracy of stereo vision systems for three-dimensional point localization. The proposed camera calibration method uses the least square method to model the error caused by the image digitalization and the lens distortion. To obtain particular three-dimensional point coordinates, the stereo vision systems use the information of two images taken by two different cameras. Then, the system locates the two-dimensional pixel coordinates of the three-dimensional point in both images and coverts them into angles. With the obtained angles, the system finds the three-dimensional point coordinates through a triangulation process. The proposed camera calibration method is applied in the stereo vision systems, and a comparative analysis between the real and calibrated three-dimensional data points is performed to validate the improvements. Moreover, the developed method is compared with three classical calibration methods to analyze their advantages in terms of accuracy with respect to tested methods.

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Section: Svs Implementationmentioning

confidence: 99%

Improve three-dimensional point localization accuracy in stereo vision systems using a novel camera calibration method

Ramírez-Hernández

Rodríguez-Quiñonez

Castro-Toscano

et al. 2020

International Journal of Advanced Robotic Systems

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“…We have compared our results with recently published methods (i.e. [4–8 ]) to show the competitiveness of the proposed method in this Letter. Their method were developed with different framework architectures including the deep learning method in [9 ].…”

Section: Introductionmentioning

confidence: 94%

Improvement of disparity map refinement stage using adaptive least square plane fitting technique

Hamzah

Ibrahim

2020

Electronics Letters

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This Letter presents an improvement of disparity map refinement stage using adaptive least square plane fitting technique. This technique is proposed to increase the accuracy on the final stage of stereo matching algorithm. Fundamentally, the accuracy of matching process depends on the robustness of an algorithm on the plain colour region, depth discontinuity and repetitive pattern area. These regions are difficult to be matched and very challenging. Thus, this Letter proposes multiple point selections of disparities on a plane to adaptively group similar disparity values. Then, the distance of each disparity is calculated to ensure the values are accurately grouped. The distance refers to an assigned reference radius disparity location. Based on the experimental results using the KITTI and Middlebury benchmark images, the proposed method is capable of increasing the accuracy on the challenging regions and the quantitative measurement.

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“…FPGA , GPU , DSP , and multicore-CPUs are widely used to accelerate the time taken by different stereo matching algorithms. Chang and Maruyama have developed a real-time stereo vision system using multi-block matching on GPU [1]. They used gray images and they calculated the pixel matching cost using Normalized Cross-Correlation (NCC).…”

Section: A Al-marakeby M Zakimentioning

confidence: 99%

Improving Processing Speed of Real Time Stereo Matching u sing Heterogenous CPU GPU Model

Al-Marakeby*,

Zaki

2020

IJITEE

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This paper presents an improvement of the processing speed of the stereo matching problem. The time required for stereo matching represents a problem for many real time applications such as robot navigation , self-driving vehicles and object tracking. In this work, a real-time stereo matching system is proposed that utilizes the parallelism of Graphics Processing Unit (GPU). An area based stereo matching system is used to generate the disparity map. Four different sequential and parallel computational models are used to analyze the time consumed by the stereo matching. The models are: 1) Sequential CPU, 2) Parallel multi-core CPU, 3) Parallel GPU and 4) Parallel heterogenous CPU/GPU. The dense disparity image is calculated, and the time is highly reduced using the heterogenous CPU/GPU model, while maintaining the same accuracy of other models. A static partitioning of CPU and GPU workload is properly designed based on time analysis. Different cost functions are used to measure the correspondence and to generate the disparity map. A sliding window is used to calculate the cost functions efficiently. A speed of more than 100 frames per second(f/s) is achieved using parallel heterogenous CPU/GPU for 640 x 480 image resolution and a disparity range equals 50.

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Real-Time Stereo Vision System: A Multi-Block Matching on GPU

Cited by 32 publications

References 44 publications

Improve three-dimensional point localization accuracy in stereo vision systems using a novel camera calibration method

Improve three-dimensional point localization accuracy in stereo vision systems using a novel camera calibration method

Improvement of disparity map refinement stage using adaptive least square plane fitting technique

Improving Processing Speed of Real Time Stereo Matching u sing Heterogenous CPU GPU Model

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