Stereo Matching Algorithm Based on Two-Phase Adaptive Optimization of AD-Census and Gradient Fusion

Liu, Hao; Zhang, Hanlong; Nie, Xiaoxi; He, Wei; Luo, Dong; Jiao, Guohua; Chen, Wei

doi:10.1109/rcar52367.2021.9517511

Cited by 7 publications

(6 citation statements)

References 15 publications

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“…This method calculates the absolute difference cost and census transformation cost of each pixel by weighting, and obtains a disparity map through cost aggregation, disparity selection, and disparity refinement. The results show that this method performs well in textureless and parallax discontinuous regions, and is sufficiently robust to radiation changes and noise [18].…”

Section: Related Workmentioning

confidence: 88%

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3D Reconstruction Cost Function Algorithm Based on Stereo Matching in the Background of Digital Museums

Peng,

Han

2023

IEEE Access

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Stereo matching plays an important role in 3D reconstruction in the context of digital museums. At present, it has problems such as occlusion, weak texture, and discontinuous disparity, which restrict the development of binocular vision. In response to this type of problem, Census transformed algorithms based on mean discrimination and Sobel edge detection were introduced to calculate the cost function. At the same time, the algorithm also incorporated the absolute value method of grayscale difference, making it more adaptable to situations such as discontinuous disparity and weak textures. The results show that the Census transform algorithm, which introduces edge gradients, has the lowest error matching rate on different images, with a minimum value of 25.1%. The classical Census transformation method and the AD Census classical transformation algorithm are 28.3% and 27.4%, respectively.Compared with the other two algorithms, the Census transformation of edge gradient improves the matching performance of the algorithm in the discontinuous area of edge disparity and improves the anti-interference ability of the algorithm. At the same time, the algorithm has the lowest error matching rate in the disparity discontinuous regions of Teddy, Cones, Venus, and Tsukuba images, and the lowest value is only 32.1%. Compared to the classic Census transformation method and the AD Census classical transformation algorithm, the minimum error matching rate has decreased by 13.2% and 4.5%, respectively. In addition, the algorithm has the lowest average effective runtime on all four types of images, with an effective average of 4.6 seconds on Venus images with rich texture features, which is 0.6 seconds lower than the classic Census transform algorithm.The improved Census transform algorithm not only has high matching accuracy but also low time complexity, providing a reliable method reference for modern 3D reconstruction fields.

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Section: Related Workmentioning

confidence: 88%

“…Then merge the horizontal and vertical gradient images. This can be achieved by calculating the amplitude of two gradient images, that is, calculating the gradient size of each pixel point, as shown in the formula (18).…”

Section: [ ] mentioning

confidence: 99%

3D Reconstruction Cost Function Algorithm Based on Stereo Matching in the Background of Digital Museums

Peng,

Han

2023

IEEE Access

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“…The proposed method is compared with ADSG [12] and Deep Pruner [24]. The results for the methods compared are obtained from Middlebury evaluation leader board.…”

Section: E Comparison With Existing Methodsmentioning

confidence: 99%

“…A star-census transform (SCT) is introduced [11] that initiates the neighborhood pixel sampling in a symmetrical order that excludes the central pixel in the matching window. An improvised AD-Census stereo matching using gradient fusion (ADSG) is introduced in [12]. The absolute difference is used along with census transform for cost calculation, the result is then combined with gradient cost.…”

Section: Introductionmentioning

confidence: 99%

An Improved Depth Estimation using Stereo Matching and Disparity Refinement Based on Deep Learning

Udupa

2023

IJACSA

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Stereo matching techniques are a vital subject in computer vision. It focuses on finding accurate disparity maps that find its use in several applications namely reconstruction of a 3D scene, navigation of robot, augmented reality. It is a method of obtaining corresponding matching point in stereo images to get disparity map. With additional details, this disparity map could be converted into a depth of a scene. Obtaining an efficient disparity map in the texture less, occluded, and discontinuous areas is a difficult job. A matching cost using an improvised Census transform and an optimization framework is proposed to produce an initial disparity map. The classic Census transform focus on the value of pixel at the center. If this pixel is prone to noisy condition, then the census encoding may differ which leads to mismatches. To overcome this issue an improved census transform based on weighted sum values of the neighborhood pixels is proposed which suppresses the noise during stereo matching. Additionally, a deep learning based disparity refinement technique using the generative adversarial network to handle texture less, occluded, and discontinuous areas is proposed. The suggested method offers cutting-edge performance in terms of both qualitative and quantitative outcomes.

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“…To verify the effectiveness of the algorithm, 15 sets of training set pictures, provided by MiddleburyV3 [13] , are used for experiments and compared with ADSG [9] , ADSM [10] , ACR-GIF-OW [11] , and ISM [8] algorithms. The experimental parameters are shown in Table 1.…”

Section: Experimental Analysismentioning

confidence: 99%

Multi-cost fusion stereo matching algorithm based on guided filter aggregation

Zhang

2023

Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)

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Aiming at the low matching accuracy of existing local stereo matching algorithms in weak texture areas, a local stereo matching algorithm based on multi-matching cost fusion and guided filtering cost aggregation with adaptive parameters is proposed. First, use the gradient direction to improve the gradient cost, and calculate the matching cost by combining the gradient cost with the Census transform and color cost. Secondly, the cost is aggregated by the guided filtering of adaptive parameters; Finally, the final disparity map is obtained through disparity calculation and multi-step disparity refinement. The improved algorithm is tested on 15 training sets on the Middlebury3 platform, and the average false matching rates of bad4.0 in all areas and non-occluded areas are 19.9% and 13.2%, respectively, which is improved compared with AD-Census and other algorithms.

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Stereo Matching Algorithm Based on Two-Phase Adaptive Optimization of AD-Census and Gradient Fusion

Cited by 7 publications

References 15 publications

3D Reconstruction Cost Function Algorithm Based on Stereo Matching in the Background of Digital Museums

3D Reconstruction Cost Function Algorithm Based on Stereo Matching in the Background of Digital Museums

An Improved Depth Estimation using Stereo Matching and Disparity Refinement Based on Deep Learning

Multi-cost fusion stereo matching algorithm based on guided filter aggregation

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