A High-Speed Low-Cost Hardware Implementation for Depth Estimation Using Disparity Fusion Method
Depth estimation using stereo images can be achieved by calculating the disparity values between the left and the right images captured by two parallel cameras. Reconstructing depth information from 2D images is crucial in many applications, such as self-driving vehicles and robot navigation. Furthe...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9817129/ |
| _version_ | 1818520904908406784 |
|---|---|
| author | You-Rong Chen Wei-Ting Chen Shao-Chieh Liao Pei-Yin Chen Hong-Yu Fang Tzu-You Tai |
| author_facet | You-Rong Chen Wei-Ting Chen Shao-Chieh Liao Pei-Yin Chen Hong-Yu Fang Tzu-You Tai |
| author_sort | You-Rong Chen |
| collection | DOAJ |
| description | Depth estimation using stereo images can be achieved by calculating the disparity values between the left and the right images captured by two parallel cameras. Reconstructing depth information from 2D images is crucial in many applications, such as self-driving vehicles and robot navigation. Furthermore, most of these applications are employed with resource-constrained devices and have real-time requirements. In this paper, a high-speed, low-cost hardware implementation for disparity estimation is proposed. We adopted the novel disparity fusion method in our architecture, which can significantly reduce the number of calculations in the overall process. A refinement method is also designed to reduce the error rate of the resulting depth map and improve the tolerance to light noise. The proposed algorithm was implemented with the Kintex-7 field-programmable gate array. Its performance was tested by using the Middlebury-Version 2 and -Version 3 datasets. The proposed algorithm provides an operating speed of 118 fps with an error rate of only 6.36%. Compared with other state-of-the-art designs used for similar applications, the proposed method can achieve a 34.6% reduction in the error rate while providing the highest speed with competitive hardware cost. |
| first_indexed | 2024-12-11T01:43:53Z |
| format | Article |
| id | doaj.art-02b56fa5860e421392ecdf56ffc6224c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-11T01:43:53Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-02b56fa5860e421392ecdf56ffc6224c2022-12-22T01:24:58ZengIEEEIEEE Access2169-35362022-01-0110728507286510.1109/ACCESS.2022.31890089817129A High-Speed Low-Cost Hardware Implementation for Depth Estimation Using Disparity Fusion MethodYou-Rong Chen0https://orcid.org/0000-0003-3935-7261Wei-Ting Chen1https://orcid.org/0000-0002-4785-6784Shao-Chieh Liao2Pei-Yin Chen3https://orcid.org/0000-0001-6561-3164Hong-Yu Fang4Tzu-You Tai5https://orcid.org/0000-0001-7365-7562Department of Computer Science and Information Engineering, Digital IC Design Laboratory, National Cheng Kung University, Tainan, TaiwanNovatek Microelectronics Corporation, Hsinchu, TaiwanDepartment of Computer Science and Information Engineering, Digital IC Design Laboratory, National Cheng Kung University, Tainan, TaiwanDepartment of Computer Science and Information Engineering, Digital IC Design Laboratory, National Cheng Kung University, Tainan, TaiwanNovatek Microelectronics Corporation, Hsinchu, TaiwanMediaTek Inc., Hsinchu, TaiwanDepth estimation using stereo images can be achieved by calculating the disparity values between the left and the right images captured by two parallel cameras. Reconstructing depth information from 2D images is crucial in many applications, such as self-driving vehicles and robot navigation. Furthermore, most of these applications are employed with resource-constrained devices and have real-time requirements. In this paper, a high-speed, low-cost hardware implementation for disparity estimation is proposed. We adopted the novel disparity fusion method in our architecture, which can significantly reduce the number of calculations in the overall process. A refinement method is also designed to reduce the error rate of the resulting depth map and improve the tolerance to light noise. The proposed algorithm was implemented with the Kintex-7 field-programmable gate array. Its performance was tested by using the Middlebury-Version 2 and -Version 3 datasets. The proposed algorithm provides an operating speed of 118 fps with an error rate of only 6.36%. Compared with other state-of-the-art designs used for similar applications, the proposed method can achieve a 34.6% reduction in the error rate while providing the highest speed with competitive hardware cost.https://ieeexplore.ieee.org/document/9817129/Depth estimationhardware implementationhigh-speedlow-coststereo matching |
| spellingShingle | You-Rong Chen Wei-Ting Chen Shao-Chieh Liao Pei-Yin Chen Hong-Yu Fang Tzu-You Tai A High-Speed Low-Cost Hardware Implementation for Depth Estimation Using Disparity Fusion Method IEEE Access Depth estimation hardware implementation high-speed low-cost stereo matching |
| title | A High-Speed Low-Cost Hardware Implementation for Depth Estimation Using Disparity Fusion Method |
| title_full | A High-Speed Low-Cost Hardware Implementation for Depth Estimation Using Disparity Fusion Method |
| title_fullStr | A High-Speed Low-Cost Hardware Implementation for Depth Estimation Using Disparity Fusion Method |
| title_full_unstemmed | A High-Speed Low-Cost Hardware Implementation for Depth Estimation Using Disparity Fusion Method |
| title_short | A High-Speed Low-Cost Hardware Implementation for Depth Estimation Using Disparity Fusion Method |
| title_sort | high speed low cost hardware implementation for depth estimation using disparity fusion method |
| topic | Depth estimation hardware implementation high-speed low-cost stereo matching |
| url | https://ieeexplore.ieee.org/document/9817129/ |
| work_keys_str_mv | AT yourongchen ahighspeedlowcosthardwareimplementationfordepthestimationusingdisparityfusionmethod AT weitingchen ahighspeedlowcosthardwareimplementationfordepthestimationusingdisparityfusionmethod AT shaochiehliao ahighspeedlowcosthardwareimplementationfordepthestimationusingdisparityfusionmethod AT peiyinchen ahighspeedlowcosthardwareimplementationfordepthestimationusingdisparityfusionmethod AT hongyufang ahighspeedlowcosthardwareimplementationfordepthestimationusingdisparityfusionmethod AT tzuyoutai ahighspeedlowcosthardwareimplementationfordepthestimationusingdisparityfusionmethod AT yourongchen highspeedlowcosthardwareimplementationfordepthestimationusingdisparityfusionmethod AT weitingchen highspeedlowcosthardwareimplementationfordepthestimationusingdisparityfusionmethod AT shaochiehliao highspeedlowcosthardwareimplementationfordepthestimationusingdisparityfusionmethod AT peiyinchen highspeedlowcosthardwareimplementationfordepthestimationusingdisparityfusionmethod AT hongyufang highspeedlowcosthardwareimplementationfordepthestimationusingdisparityfusionmethod AT tzuyoutai highspeedlowcosthardwareimplementationfordepthestimationusingdisparityfusionmethod |