Research on the Improvement of Semi-Global Matching Algorithm for Binocular Vision Based on Lunar Surface Environment

Guo, Yongchun; Gu, Mengjiao; Xu, Zhao-Dong

doi:10.3390/s23156901

Cited by 4 publications

References 42 publications

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Cell-Based Refinement Processor Utilizing Disparity Characteristics of Road Environment for SGM-Based Stereo Vision Systems

Choi,

Oh,

Han

et al. 2023

IEEE Access

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Embedded stereo vision systems based on traditional approaches often require a disparity refinement process to enhance image quality. Weighted median filter (WMF)-based processors are commonly employed for their excellent refinement performance. However, when implemented on a field-programmable gate array (FPGA), WMF-based processors face a trade-off between hardware resource utilization and refinement performance. To address this trade-off, we previously proposed a new disparity refinement processor based on the hybrid max-median filter (HMMF). However, our earlier work did not guarantee flawless operation in large occluded and texture-less regions, particularly in areas with numerous holes. In order to overcome this limitation of conventional processors, we proposed a cell-based disparity refinement processor. This processor extends our previous HMMF-based disparity refinement processor. To evaluate its refinement performance, we conducted experiments using four types of publicly available stereo datasets. When comparing refinement performance, our proposed processor outperforms conventional processors when using the KITTI 2012 and 2015 stereo benchmark datasets. Additionally, the results demonstrate that our proposed processor exhibits superior refinement performance when applied to the Cityscapes and StereoDriving datasets in comparison to conventional processors. Furthermore, when considering hardware resource utilization, our proposed processor demonstrates lower resource requirements than conventional processors when implemented on an FPGA. Therefore, our proposed disparity refinement processor is wellsuited for the disparity refinement process in stereo vision systems that require cost-effectiveness and high performance.INDEX TERMS Stereo vision, disparity refinement, hardware architecture, FPGA I. INTRODUCTION

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Cell-Based Refinement Processor Utilizing Disparity Characteristics of Road Environment for SGM-Based Stereo Vision Systems

Choi,

Oh,

Han

et al. 2023

IEEE Access

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Design and Characterisation of Stereo Endoscope for Polyp Size Measurement

GS,

Dave,

et al. 2024

2024 IEEE International Symposium on Medical Measurements and Applications (MeMeA)

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High-Precision Disparity Estimation for Lunar Scene Using Optimized Census Transform and Superpixel Refinement

Liang,

Long,

Zhu

et al. 2024

Remote Sensing

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High-precision lunar scene 3D data are essential for lunar exploration and the construction of scientific research stations. Currently, most existing data from orbital imagery offers resolutions up to 0.5–2 m, which is inadequate for tasks requiring centimeter-level precision. To overcome this, our research focuses on using in situ stereo vision systems for finer 3D reconstructions directly from the lunar surface. However, the scarcity and homogeneity of available lunar surface stereo datasets, combined with the Moon’s unique conditions—such as variable lighting from low albedo, sparse surface textures, and extensive shadow occlusions—pose significant challenges to the effectiveness of traditional stereo matching techniques. To address the dataset gap, we propose a method using Unreal Engine 4 (UE4) for high-fidelity physical simulation of lunar surface scenes, generating high-resolution images under realistic and challenging conditions. Additionally, we propose an optimized cost calculation method based on Census transform and color intensity fusion, along with a multi-level super-pixel disparity optimization, to improve matching accuracy under harsh lunar conditions. Experimental results demonstrate that the proposed method exhibits exceptional robustness and accuracy in our soon-to-be-released multi-scene lunar dataset, effectively addressing issues related to special lighting conditions, weak textures, and shadow occlusion, ultimately enhancing disparity estimation accuracy.

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Research on the Improvement of Semi-Global Matching Algorithm for Binocular Vision Based on Lunar Surface Environment

Cited by 4 publications

References 42 publications

Cell-Based Refinement Processor Utilizing Disparity Characteristics of Road Environment for SGM-Based Stereo Vision Systems

Cell-Based Refinement Processor Utilizing Disparity Characteristics of Road Environment for SGM-Based Stereo Vision Systems

Design and Characterisation of Stereo Endoscope for Polyp Size Measurement

High-Precision Disparity Estimation for Lunar Scene Using Optimized Census Transform and Superpixel Refinement

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