Disparity Refinement Processor Architecture Utilizing Horizontal and Vertical Characteristics for Stereo Vision Systems
Cheol‐Ho Choi, Hyun Woo Oh · 2023
In embedded stereo vision systems based on semi-global matching, the matching accuracy of the initial disparity map can be degraded because of various factors. To solve this problem, weighted median-based disparity refinement hardware architectures are utilized to improve the matching accuracy. However, for the conventional hardware architectures, there is a trade-off between hardware resource utilization and re-finement performance when they are implemented on a field programmable gate array (FPGA). Therefore, in this paper, we propose a hybrid max-median filter and its hardware architecture to improve the refinement performance and reduce hardware resource utilization. To evaluate the refinement performance, we used two public stereo datasets. When using the various window sizes for KITTI 2012 and 2015 stereo benchmark datasets, the proposed hardware architecture showed better matching accuracy performance compared with the conventional hardware architectures. In terms of the hardware resource utilization, when implemented on an FPGA, the proposed hardware architecture has low requirements for all types of hardware resources. That is, the proposed hardware architecture overcomes the trade-off between hardware resource utilization and refinement performance.