MAP Disparity Estimation Using Hidden Markov Trees

Psota, Eric T.; Kowalczuk, Jȩdrzej; Mittek, Mateusz; Pérez, Lance C.

doi:10.1109/iccv.2015.256

Cited by 22 publications

(13 citation statements)

References 28 publications

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“…This work was later extended and implemented on graphics hardware for real-time performance [8]. Psota et al [28] utilized Hidden Markov Trees (HMT) to create minimum spanning trees based on color information which allows aggregated costs to be passed along the tree branches, and the isolated mismatches were later moved by median filtering.…”

Section: Related Workmentioning

confidence: 99%

“…Since a large set of (a) training (b) testing Figure 9. Comparison with the top ten approaches on the Middlebury Stereo Evaluation site [29]: SED [23], R-NCC (unpublished work), r200high [15], ICSG [33], SGM [11], DF (unpublished work), MotionStereo (unpublished work), IDR [17], TMAP [28] and SNCC [6]. Performances of different approaches on both training (a) and testing (b) datasets are plotted on non-occlusion error rates v.s.…”

Section: Matching-net Evaluation-net Attributes Kernel Size Quantitymentioning

confidence: 99%

See 1 more Smart Citation

Semi-Dense Stereo Matching Using Dual CNNs

Mao

Wang

Zhou

et al. 2019

2019 IEEE Winter Conference on Applications of Computer Vision (WACV)

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A robust solution for semi-dense stereo matching is presented. It utilizes two CNN models for computing stereo matching cost and performing confidence-based filtering, respectively. Compared to existing CNNs-based matching cost generation approaches, our method feeds additional global information into the network so that the learned model can better handle challenging cases, such as lighting changes and lack of textures. Through utilizing nonparametric transforms, our method is also more self-reliant than most existing semi-dense stereo approaches, which rely highly on the adjustment of parameters. The experimental results based on Middlebury Stereo dataset demonstrate that the proposed approach outperforms the state-of-the-art semi-dense stereo approaches.

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Section: Related Workmentioning

confidence: 99%

Section: Matching-net Evaluation-net Attributes Kernel Size Quantitymentioning

confidence: 99%

Semi-Dense Stereo Matching Using Dual CNNs

Mao

Wang

Zhou

et al. 2019

2019 IEEE Winter Conference on Applications of Computer Vision (WACV)

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show abstract

“…3) Post-processing: We follow the same post-processing scheme as mentioned in the PMS [8]. Additionally, we apply a median filter on each pixel of the resulting disparity map to eliminate any isolated mismatches [36].…”

Section: ) Initialisationmentioning

confidence: 99%

“…Our unoptimised C implementation of PMS takes around 150 minutes to process an image of size 900 × 750 pixels whereas IPMS is approximately five times more expensive but still comparable to global methods such as [36]. The main reason for the additional computational time is the inclusion of the search image support weight in the cost function, which reduces false matching in occluded regions.…”

Section: B Comparison With Pmsmentioning

confidence: 99%

Constrained Optimization for Plane-Based Stereo

Ahmed

Hansard

Cavallaro

2018

IEEE Trans. on Image Process.

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Depth and surface normal estimation are crucial components in understanding 3D scene geometry from calibrated stereo images. In this paper, we propose visibility and disparity magnitude constraints for slanted patches in the scene. These constraints can be used to associate geometrically feasible planes with each point in the disparity space. The new constraints are validated in the PatchMatch Stereo framework. We use these new constraints not only for initialization, but also in the local plane refinement step of this iterative algorithm. The proposed constraints increase the probability of estimating correct plane parameters, and lead to an improved 3D reconstruction of the scene. Furthermore, the proposed constrained initialization reduces the number of iterations before convergence to the optimal plane parameters. In addition, as most stereo image pairs are not perfectly rectified, we modify the view propagation process by assigning the plane parameters to the neighbors of the candidate pixel. To update the plane parameters in the plane refinement step, we use a gradient free non-linear optimizer. The benefits of the new initialization, propagation, and refinement schemes are demonstrated.

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“…At the same time, the need for high resolution stereo images is on the rise [18,19]. However, there is a lack of research on imperfect rectification in stereo matching and most previous studies [20][21][22][23][24][25][26][27][28] are not aware of the problem of high resolution images.…”

Section: Introductionmentioning

confidence: 99%

Stereo Matching Methods for Imperfectly Rectified Stereo Images

Nguyen

Ahn

2019

Symmetry

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Stereo matching has been under development for decades and is an important process for many applications. Difficulties in stereo matching include textureless regions, occlusion, illumination variation, the fattening effect, and discontinuity. These challenges are effectively solved in recently developed stereo matching algorithms. A new imperfect rectification problem has recently been encountered in stereo matching, and the problem results from the high resolution of stereo images. State-of-the-art stereo matching algorithms fail to exactly reconstruct the depth information using stereo images with imperfect rectification, as the imperfectly rectified image problems are not explicitly taken into account. In this paper, we solve the imperfect rectification problems, and propose matching stereo matching methods that based on absolute differences, square differences, normalized cross correlation, zero-mean normalized cross correlation, and rank and census transforms. Finally, we conduct experiments to evaluate these stereo matching methods using the Middlebury datasets. The experimental results show the proposed stereo matching methods can reduce error rate significantly for stereo images with imperfect rectification.

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MAP Disparity Estimation Using Hidden Markov Trees

Cited by 22 publications

References 28 publications

Semi-Dense Stereo Matching Using Dual CNNs

Semi-Dense Stereo Matching Using Dual CNNs

Constrained Optimization for Plane-Based Stereo

Stereo Matching Methods for Imperfectly Rectified Stereo Images

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