GigaDepth: Learning Depth from Structured Light with Branching Neural Networks
暂无分享,去创建一个
[1] Mohammad Mahdi Johari,et al. DepthInSpace: Exploitation and Fusion of Multiple Video Frames for Structured-Light Depth Estimation , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[2] Sylvain Paris,et al. Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[3] Ian Reid,et al. Unsupervised Scale-Consistent Depth Learning from Video , 2021, International Journal of Computer Vision.
[4] Oisin Mac Aodha,et al. The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[5] Vladlen Koltun,et al. Vision Transformers for Dense Prediction , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[6] Bin Li,et al. PENet: Towards Precise and Efficient Image Guided Depth Completion , 2021, 2021 IEEE International Conference on Robotics and Automation (ICRA).
[7] S. Izadi,et al. HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[8] Zhaoyang Wang,et al. Single-Shot 3D Shape Reconstruction Using Structured Light and Deep Convolutional Neural Networks , 2020, Sensors.
[9] Kiriakos N. Kutulakos,et al. Auto-Tuning Structured Light by Optical Stochastic Gradient Descent , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[10] Sam Van der Jeught,et al. Deep neural networks for single shot structured light profilometry. , 2019, Optics express.
[11] Gernot Riegler,et al. Connecting the Dots: Learning Representations for Active Monocular Depth Estimation , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[12] Luc Van Gool,et al. Sparse and Noisy LiDAR Completion with RGB Guidance and Uncertainty , 2019, 2019 16th International Conference on Machine Vision Applications (MVA).
[13] Gabriel J. Brostow,et al. Digging Into Self-Supervised Monocular Depth Estimation , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[14] Mohit Gupta,et al. A Geometric Perspective on Structured Light Coding , 2018, ECCV.
[15] Shahram Izadi,et al. StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction , 2018, ECCV.
[16] Yinda Zhang,et al. ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo Systems , 2018, ECCV.
[17] Kiriakos N. Kutulakos,et al. Optimal Structured Light a la Carte , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[18] Xiaoou Tang,et al. LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[19] Yinda Zhang,et al. Deep Depth Completion of a Single RGB-D Image , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[20] Yong-Sheng Chen,et al. Pyramid Stereo Matching Network , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[21] Sergio Orts,et al. HyperDepth: Learning Depth from Structured Light without Matching , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[22] Leonidas J. Guibas,et al. ShapeNet: An Information-Rich 3D Model Repository , 2015, ArXiv.
[23] Vladlen Koltun,et al. Fast MRF Optimization with Application to Depth Reconstruction , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[24] Rainer Stiefelhagen,et al. Kinect Unleashed: Getting Control over High Resolution Depth Maps , 2013, MVA.
[25] H. Hirschmüller. Accurate and Efficient Stereo Processing by Semi-Global Matching and Mutual Information , 2005, CVPR.
[26] Robert A. Hummel,et al. Experiments with the intensity ratio depth sensor , 1985, Comput. Vis. Graph. Image Process..
[27] Martin D. Altschuler,et al. The Numerical Stereo Camera , 1981, Other Conferences.