Hardware-friendly architecture for a pseudo 2D weighted median filter based on sparse-window approach

Hyun, Jongkil; Kim, Younghyeon; Kim, Junghwan; Moon, Byungin

doi:10.1007/s11042-020-09906-2

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…For instance, the matching cost function utilized in the algorithm can generate false-positive signals, which eventuates in the creation of depth maps with low accuracy. Thus, the use of post-processing approaches (i.e., median filter, bilateral filter, and interpolation) is of great importance in stereo vision applications to delete noise and refine depth maps [ 59 , 60 , 61 , 62 ].…”

Section: Depth Estimation (De)mentioning

confidence: 99%

Monocular Depth Estimation Using Deep Learning: A Review

Masoumian

Rashwan

Cristiano

et al. 2022

Sensors

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In current decades, significant advancements in robotics engineering and autonomous vehicles have improved the requirement for precise depth measurements. Depth estimation (DE) is a traditional task in computer vision that can be appropriately predicted by applying numerous procedures. This task is vital in disparate applications such as augmented reality and target tracking. Conventional monocular DE (MDE) procedures are based on depth cues for depth prediction. Various deep learning techniques have demonstrated their potential applications in managing and supporting the traditional ill-posed problem. The principal purpose of this paper is to represent a state-of-the-art review of the current developments in MDE based on deep learning techniques. For this goal, this paper tries to highlight the critical points of the state-of-the-art works on MDE from disparate aspects. These aspects include input data shapes and training manners such as supervised, semi-supervised, and unsupervised learning approaches in combination with applying different datasets and evaluation indicators. At last, limitations regarding the accuracy of the DL-based MDE models, computational time requirements, real-time inference, transferability, input images shape and domain adaptation, and generalization are discussed to open new directions for future research.

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Section: Depth Estimation (De)mentioning

confidence: 99%

Monocular Depth Estimation Using Deep Learning: A Review

Masoumian

Rashwan

Cristiano

et al. 2022

Sensors

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“…Recent research aims to alleviate hardware resource requirements by reducing computational complexity. To achieve this, Chen et al proposed a separable WMF (sWMF), and subsequently, Hyun et al proposed a sparse-window-approachbased sWMF (ssWMF) to further decrease hardware resource utilization compared to the sWMF [28], [29]. While these subsequent studies succeed in reducing hardware resource utilization when implemented on an FPGA, they often come at the cost of degraded refinement performance, revealing a trade-off between hardware resource utilization and refinement performance.…”

mentioning

confidence: 99%

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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Disparity Refinement Processor Architecture Utilizing Horizontal and Vertical Characteristics for Stereo Vision Systems

Choi,

2023

2023 26th Euromicro Conference on Digital System Design (DSD)

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Hardware-friendly architecture for a pseudo 2D weighted median filter based on sparse-window approach

Cited by 3 publications

References 14 publications

Monocular Depth Estimation Using Deep Learning: A Review

Monocular Depth Estimation Using Deep Learning: A Review

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

Disparity Refinement Processor Architecture Utilizing Horizontal and Vertical Characteristics for Stereo Vision Systems

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