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Generative Adversarial Networks for Depth Map Estimation from RGB Video
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018Depth cues are essential to achieving high-level scene understanding, and in particular to determining geometric relations between objects. The ability to reason about depth information in scene analysis tasks can often result in improved decision-making
Kin Gwn Lore +3 more
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Motion-Depth: RGB-D Depth Map Enhancement with Motion and Depth in Complement
2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014Low-cost RGB-D imaging system such as Kinect is widely utilized for dense 3D reconstruction. However, RGB-D system generally suffers from two main problems. The spatial resolution of the depth image is low. The depth image often contains numerous holes where no depth measurements are available. This can be due to bad infra-red reflectance properties of
Tak-Wai Hui, King Ngi Ngan
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Optimized adaptive depth map filtering
2013 IEEE International Conference on Image Processing, 2013Three-Dimensional (3-D) Television (TV) is believed to be the future TV broadcasting technology which then would replace the traditional 2-D TV systems. Future 3-D TV could enhance the visual home entertainment experience in a way that the user can navigate through the scene or switch the viewpoint.
Martin Köppel +2 more
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No-reference quality metric for depth maps
2013 IEEE International Conference on Image Processing, 2013The performances of several 3D imaging/video applications (going from 3DTV to video surveillance) benefit from the estimation or acquisition of accurate and high quality depth maps. However, the characteristics of depth information is strongly affected by the procedure employed in its acquisition or estimation (e.g., stereo evaluation, ToF cameras ...
MILANI, SIMONE, D. Ferrario, S. Tubaro
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Interactive Floor Mapping with Depth Sensors
2021Simultaneous Localization and Mapping (SLAM) algorithms have been growing popular in indoor navigation and mapping. However, it often fails in many real-world environments, such as low-lighting, fast motion, featureless walls, and large buildings. There are also usability issues with the 3D point clouds for actual indoor localization and mapping for ...
Yang Cai, Uma Arunachalam
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The refined Moho depth map in the Carpathian-Pannonian region
Contributions to Geophysics and Geodesy, 2018We present a new digital Moho depth map of the Carpathian-Pannonian region. The map was produced by compiling Moho discontinuity depth data, which were obtained by interpretation of seismic measurements taking into account the results of 2-D and 3-D ...
M. Bielik +10 more
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IEEE Transactions on Image Processing, 2018
Guided depth map enhancement based on Markov random field (MRF) normally assumes edge consistency between the color image and the corresponding depth map.
Y. Zuo, Qiang Wu, Jian Zhang, P. An
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Guided depth map enhancement based on Markov random field (MRF) normally assumes edge consistency between the color image and the corresponding depth map.
Y. Zuo, Qiang Wu, Jian Zhang, P. An
semanticscholar +1 more source
Fast Depth Map Super-Resolution Using Deep Neural Network
2018 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), 2018Depth map super-resolution is a challenging computer vision problem. In this paper, we present two deep convolutional neural networks solving the problem of single depth map super-resolution.
A. Korinevskaya, Ilya Makarov
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Dynamic Guidance for Depth Map Restoration
2019 IEEE 21st International Workshop on Multimedia Signal Processing (MMSP), 2019The guidance of color images greatly improves the restoration accuracy of the depth map. However, due to the incomplete texture structure consistency between the color image and the depth map, and the limitations of $L^{2}$ based model, static guidance tends to cause texture copy artifacts and blurring depth discontinuities, which seriously ...
Ran Zhu +3 more
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Fast Semi-dense Depth Map Estimation
RETech@ICMR, 2018We consider the problem of depth reconstruction from downsampled sparse depth values. We compare our approach with semi-dense depth map interpolation and direct RGB-to-Depth reconstruction solutions on several datasets, including Matterport 3D dataset ...
Ilya Makarov +2 more
semanticscholar +1 more source