Deeper Depth Prediction with Fully Convolutional Residual Networks

Keywords

Computer science, Residual, Artificial intelligence, Convolutional neural network, Depth map

References

• Modeling the Shape of the Scene: A Holistic Representation of the Spatial EnvelopeAude Oliva, Antonio Torralba — 2001
• Very Deep Convolutional Networks for Large-Scale Image RecognitionKaren Simonyan, Andrew Zisserman — 2014
• Learning to generate chairs with convolutional neural networksAlexey Dosovitskiy, Jost Tobias Springenberg, Thomas Brox — 2014
• Fully convolutional networks for semantic segmentationJonathan Long, Evan Shelhamer, Trevor Darrell — 2014
• Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional ArchitectureDavid Eigen, Rob Fergus — 2014
• Towards unified depth and semantic prediction from a single imagePeng Wang, Xiaohui Shen, Zhe Lin — 2015
• Depth from focus with your mobile phoneSupasorn Suwajanakorn, Carlos Hernández, Steven M. Seitz — 2015
• Fast bilateral-space stereo for synthetic defocusJonathan T. Barron, Andrew Adams, YiChang Shih — 2015
• MatConvNet: Convolutional Neural Networks for MATLABAndrea Vedaldi, Karel Lenc — 2014
• Pulling Things out of PerspectiveLubor Ladicky, Jianbo Shi, Marc Pollefeys — 2014
• Robust odometry estimation for RGB-D camerasChristian Kerl, Jürgen Sturm, Daniel Cremers — 2013
• Real-Time 3D Reconstruction in Dynamic Scenes Using Point-Based FusionMaik Keller, Damien Lefloch, Martin Lambers — 2013
• RGB-(D) scene labeling: Features and algorithmsXiaofeng Ren, Liefeng Bo, D. Fox — 2012
• Discrete-Continuous Depth Estimation from a Single ImageMiaomiao Liu, Mathieu Salzmann, Xuming He — 2014
• SIFT Flow: Dense Correspondence across Scenes and Its ApplicationsCe Liu, Jenny Yuen, Antonio Torralba — 2011
• Robust Optimization for Deep RegressionVasileios Belagiannis, Christian Rupprecht, Gustavo Carneiro — 2015
• ImageNet Large Scale Visual Recognition ChallengeOlga Russakovsky, Jia Deng, Hao Su — 2014
• SLIC Superpixels Compared to State-of-the-Art Superpixel MethodsRadhakrishna Achanta, Anil Shaji, Kevin Smith — 2012
• Shape from Shading: A SurveyRuo Zhang, Ping-Sing Tsai, J.E. Cryer — 1999
• 2D-to-3D image conversion by learning depth from examplesJanusz Konrad, Meng Wang, Prakash Ishwar — 2012

Cited by

• Monocular Depth Estimation Using Multi-Scale Continuous CRFs as Sequential Deep NetworksMark Elert, Kemakta Konsult Ab — 2018
• Estimating Depth From Monocular Images as Classification Using Deep Fully Convolutional Residual NetworksYuanzhouhan Cao, Zifeng Wu, Chunhua Shen — 2016
• Unsupervised Monocular Depth Estimation with Left-Right ConsistencyClément Godard, Oisin Mac Aodha, Gabriel Brostow — 2016
• Geometry-based next frame prediction from monocular videoReza Mahjourian, Martin Wicke, Anelia Angelova — 2016
• Parse geometry from a line: Monocular depth estimation with partial laser observationYiyi Liao, Lichao Huang, Yue Wang — 2016
• DeMeshNet: Blind Face Inpainting for Deep MeshFace VerificationShu Zhang, Ran He, Zhenan Sun — 2016
• Learning in an Uncertain World: Representing Ambiguity Through Multiple HypothesesChristian Rupprecht, Iro Laina, Robert DiPietro — 2016
• Semi-Supervised Deep Learning for Monocular Depth Map PredictionYevhen Kuznietsov, Jörg Stückler, Bastian Leibe — 2017
• Sparse depth sensing for resource-constrained robotsFangchang Ma, Luca Carlone, Ulaş Ayaz — 2017
• Multi-scale Continuous CRFs as Sequential Deep Networks for Monocular Depth EstimationDan Xu, Elisa Ricci, Wanli Ouyang — 2017
• CNN-SLAM: Real-Time Dense Monocular SLAM with Learned Depth PredictionKeisuke Tateno, Federico Tombari, Iro Laina — 2017
• Unsupervised Learning of Depth and Ego-Motion from VideoTinghui Zhou, Matthew Brown, Noah Snavely — 2017
• Surface Normals in the WildWeifeng Chen, Donglai Xiang, Jia Deng — 2017
• Learning 3D Object Categories by Looking Around ThemDavid Novotný, Diane Larlus, Andrea Vedaldi — 2017
• Recurrent Scene Parsing with Perspective Understanding in the LoopShu Kong, Charless C. Fowlkes — 2017
• Dense Transformer NetworksLi Jun, Yongjun Chen, Lei Cai — 2017
• Joint prediction of depths, normals and surface curvature from RGB images using CNNsThanuja Dharmasiri, Andrew Spek, Tom Drummond — 2017
• Relative Depth Order Estimation Using Multi-Scale Densely Connected Convolutional NetworksRuoxi Deng, Tianqi Zhao, Chunhua Shen — 2017
• cvpaper.challenge in 2016: Futuristic Computer Vision through 1, 600 Papers SurveyHirokatsu Kataoka, Soma Shirakabe, Yun He — 2017
• Efficient and Robust Cell Detection: A Structured Regression ApproachYuanpu Xie, Fuyong Xing, Xiaoshuang Shi — 2017

Related papers

• Make3D: Learning 3D Scene Structure from a Single Still ImageAmit Saxena, Min Sun, A.Y. Ng — 2009
• Towards unified depth and semantic prediction from a single imagePeng Wang, Xiaohui Shen, Zhe Lin — 2015
• Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional ArchitectureDavid Eigen, Rob Fergus — 2014
• Monocular Depth Estimation Using Neural Regression ForestAnirban Roy, Siniša Todorović — 2016
• Deep Residual Learning for Image RecognitionKaiming He, Xiangyu Zhang, Shaoqing Ren — 2015
• Monocular Depth Estimation Based on Multi-Scale Depth Map FusionYang Xin, Qingling Chang, Xinglin Liu — 2021