DISCO: Depth Inference from Stereo using Context

Swami, Kunal; Raghavan, Kaushik; Pelluri, Nikhilanj; Sarkar, Rituparna; Bajpai, Pankaj

doi:10.1109/icme.2019.00093

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…8 demonstrates the practical utility of AcED in the challenging single camera bokeh application. AcED was first trained using our in-house synthetic dataset [32] containing realistic human centric images with dense depth ground-truth. To reduce the computation load for this task, the light weight MobileNet V2 [33] model was employed as the backbone encoder network.…”

Section: Resultsmentioning

confidence: 99%

AcED: Accurate and Edge-consistent Monocular Depth Estimation

Swami¹,

Bondada²,

Bajpai³

2020

Preprint

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Single image depth estimation is a challenging problem. The current state-of-the-art method formulates the problem as that of ordinal regression. However, the formulation is not fully differentiable and depth maps are not generated in an end-to-end fashion. The method uses a nave threshold strategy to determine per-pixel depth labels, which results in significant discretization errors. For the first time, we formulate a fully differentiable ordinal regression and train the network in end-to-end fashion. This enables us to include boundary and smoothness constraints in the optimization function, leading to smooth and edge-consistent depth maps. A novel per-pixel confidence map computation for depth refinement is also proposed. Extensive evaluation of the proposed model on challenging benchmarks reveals its superiority over recent state-of-the-art methods, both quantitatively and qualitatively. Additionally, we demonstrate practical utility of the proposed method for single camera bokeh solution using in-house dataset of challenging real-life images.

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Section: Resultsmentioning

confidence: 99%

AcED: Accurate and Edge-consistent Monocular Depth Estimation

Swami¹,

Bondada²,

Bajpai³

2020

Preprint

Self Cite

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“…Current (as of December 2019) top 10 depth estimation methods tested in Middlebury Stereo Evaluation Version 3 [51] for the Teddy sequence achieve the average error smaller than 1.36 and the percentage of bad pixels smaller than 5.57% (E T = 4.0) (e.g. methods described in [72], [73] and [74], see Table 6). Therefore, the proposed method shows state-of-the-art results in terms of the depth maps accuracy.…”

Section: Tablementioning

confidence: 99%

Depth Map Estimation for Free-Viewpoint Television and Virtual Navigation

2020

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The paper presents a new method of depth estimation, dedicated for free-viewpoint television (FTV) and virtual navigation (VN). In this method, multiple arbitrarily positioned input views are simultaneously used to produce depth maps characterized by high interview and temporal consistencies. The estimation is performed for segments and their size is used to control the trade-off between the quality of depth maps and the processing time of depth estimation. Additionally, an original technique is proposed for the improvement of temporal consistency of depth maps. This technique uses the temporal prediction of depth, thus depth is estimated for P-type depth frames. For such depth frames, temporal consistency is high, whereas estimation complexity is relatively low. Similarly, as for video coding, I-type depth frames with no temporal depth prediction are used in order to achieve robustness. Moreover, we propose a novel parallelization technique that significantly reduces the estimation time. The method is implemented in C++ software that is provided together with this paper, so other researchers may use it as a new reference for their future works. In performed experiments, MPEG methodology was used whenever possible. The provided results demonstrate the advantages over the Depth Estimation Reference Software (DERS) developed by MPEG. The fidelity of a depth map, measured by the quality of synthesized views, is higher on average by 2.6 dB. This significant quality improvement is obtained despite a significant reduction of the estimation time, on average 4.5 times. The application of the proposed temporal consistency enhancement method increases this reduction to 29 times. Moreover, the proposed parallelization results in the reduction of the estimation time up to 130 times (using 6 threads). As there is no commonly accepted measure of the consistency of depth maps, the application of compression efficiency of depth is proposed as a measure of depth consistency.

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“…Chang et al proposed a pyramid stereo matching network [7] comprising a spatial pyramid pooling module and a 3D CNN module. Kunal Swami et al [8] proposed an end-to-end network model to utilize rich multiscale context information, which most existing methods cannot achieve. A large effective receiving domain is implemented to extract multiscale context information, while retaining the required spatial information in the entire network.…”

Section: Introductionmentioning

confidence: 99%

Local Stereo Matching Using Adaptive Cross-Region-Based Guided Image Filtering with Orthogonal Weights

Kong

Zhu

Ying

2021

Mathematical Problems in Engineering

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Adaptive cross-region-based guided image filtering (ACR-GIF) is a commonly used cost aggregation method. However, the weights of points in the adaptive cross-region (ACR) are generally not considered, which affects the accuracy of disparity results. In this study, we propose an improved cost aggregation method to address this issue. First, the orthogonal weight is proposed according to the structural feature of the ACR, and then the orthogonal weight of each point in the ACR is computed. Second, the matching cost volume is filtered using ACR-GIF with orthogonal weights (ACR-GIF-OW). In order to reduce the computing time of the proposed method, an efficient weighted aggregation computing method based on orthogonal weights is proposed. Additionally, by combining ACR-GIF-OW with our recently proposed matching cost computation method and disparity refinement method, a local stereo matching algorithm is proposed as well. The results of Middlebury evaluation platform show that, compared with ACR-GIF, the proposed cost aggregation method can significantly improve the disparity accuracy with less additional time overhead, and the performance of the proposed stereo matching algorithm outperforms other state-of-the-art local and nonlocal algorithms.

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DISCO: Depth Inference from Stereo using Context

Cited by 6 publications

References 22 publications

AcED: Accurate and Edge-consistent Monocular Depth Estimation

AcED: Accurate and Edge-consistent Monocular Depth Estimation

Depth Map Estimation for Free-Viewpoint Television and Virtual Navigation

Local Stereo Matching Using Adaptive Cross-Region-Based Guided Image Filtering with Orthogonal Weights

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