Dense stereo based on the uniqueness constraint

Stefano, Luigi Di; Marchionni, M.; Mattoccia, Stefano; Neri, Giovanni

doi:10.1109/icpr.2002.1048024

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…First, instead of visiting each pixel at each disparity, which induces a severe computational cost, libSGM [23] introduces the conception of the uniqueness ratio. As described in Formula (6), the uniqueness constraint works on the ratio of the minimum aggregated cost AggrCost min1 and the second minimum aggregated cost AggrCost min2 [28]. The matched disparity is reliable when the ratio exceeds the constraint (UNIQ).…”

Section: Disparity Computation and Refinementmentioning

confidence: 99%

RT-libSGM: FPGA-Oriented Real-Time Stereo Matching System with High Scalability

Wei

Kuno²,

Arai

et al. 2023

IEICE Trans. Inf. & Syst.

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Stereo depth estimation has become an attractive topic in the computer vision field. Although various algorithms strive to optimize the speed and the precision of estimation, the energy cost of a system is also an essential metric for an embedded system. Among these various algorithms, Semi-Global Matching (SGM) has been a popular choice for some real-world applications because of its accuracy-and-speed balance. However, its power consumption makes it difficult to be applied to an embedded system. Thus, we propose a robust stereo matching system, RT-libSGM, working on the Xilinx Field-Programmable Gate Array (FPGA) platforms. The dedicated design of each module optimizes the speed of the entire system while ensuring the flexibility of the system structure. Through an evaluation on a Zynq FPGA board called M-KUBOS, RT-libSGM achieves state-of-the-art performance with lower power consumption. Compared with the benchmark design (libSGM) working on the Tegra X2 GPU, RT-libSGM runs more than 2× faster at a much lower energy cost.

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Section: Disparity Computation and Refinementmentioning

confidence: 99%

RT-libSGM: FPGA-Oriented Real-Time Stereo Matching System with High Scalability

Wei

Kuno²,

Arai

et al. 2023

IEICE Trans. Inf. & Syst.

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“…The computational cost of fixed-window algorithm is very small, thus, fixed-window algorithm is widely adopted in practice and it is probably the most frequently used algorithm for real applications. Although there are many methods try to detecting unreliable matches of local algorithms [2,3,4] , most of them didn't give the method to rebuild the unreliable areas. This paper presents a fast stereo matching algorithm based on fixed-window algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Fast Stereo Matching Algorithm Based on Fixed-Window

Zheng

Jiang

2013

AMM

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This paper presents a fast local stereo algorithm that suitable to real time applications. Thanks to the techniques like Box-filtering, fixed-window-based stereo matching algorithms can be really fast, but perform not well in some areas, i.e. the repetitive pattern and low texture areas. In order to improve the reliability of fixed-window algorithm and keep the algorithms speed, the proposed approach can deal with the repetitive pattern and low texture areas at a small computational cost. Experimental results show that the proposed approach provides a big improvement in accuracy compare to fixed-window algorithm, and the speed of the algorithm is still fast.

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