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Given the recent advances in depth prediction from Convolutional Neural Networks (CNNs), this paper investigates how predicted depth maps from a deep neural network can be deployed for accurate and dense monocular reconstruction.
Geometric context from a single image
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A dynamic bayesian network model for autonomous 3d reconstruction from a single indoor image
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Parallel Tracking and Mapping for Small AR Workspaces
G. Klein and D. Murray · 2007
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Single image depth estimation from predicted semantic labels
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R. Kuemmerle, G. Grisetti, H. Strasdat, K. Konolige, and W. Burgard · 2011
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KinectFusion: Real-time dense surface mapping and tracking
R. A. Newcombe, A. J. Davison, S. Izadi, P. Kohli, O. Hilliges, J. Shotton, D. Molyneaux, S. Hodges, D. Kim, and A. Fitzgibbon · 2011
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Dtam: Dense tracking and mapping in real-time
R. A. Newcombe, S. Lovegrove, and A. J. Davison · 2011
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Indoor segmentation and support inference from rgbd images
N. Silberman, D. Hoiem, P. Kohli, and R. Fergus · 2012
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A benchmark for the evaluation of RGB-D SLAM systems
J. Sturm, N. Engelhard, F. Endres, W. Burgard, and D. Cremers · 2012
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Semi-dense visual odometry for a monocular camera
J. Engel, J. Sturm, and D. Cremers · 2013
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Real-Time 3D Reconstruction in Dynamic Scenes Using Point-Based Fusion
M. Keller, D. Lefloch, M. Lambers, S. Izadi, T. Weyrich, and A. Kolb · 2013
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Prediction from a single image using a multi-scale deep network
D. Eigen, C. Puhrsch, and R. Fergus · 2014
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LSD-SLAM: Large-Scale Direct Monocular SLAM
J. Engel, T. Schöps, and D. Cremers · 2014
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A benchmark for RGB-D visual odometry, 3D reconstruction and SLAM
A. Handa, T. Whelan, J. McDonald, and A. Davison · 2014
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Unsupervised feature learning for 3d scene labeling
K. Lai, L. Bo, and D. Fox · 2014
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Deep convolutional neural fields for depth estimation from a single image
F. Liu, C. Shen, and G. Lin · 2015
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Orb-slam: A versatile and accurate monocular slam system
R. Mur-Artal, J. M. M. Montiel, and J. D. Tardós · 2015
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ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
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Real-time and scalable incremental segmentation on dense slam
K. Tateno, F. Tombari, and N. Navab · 2015
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Incremental dense semantic stereo fusion for large-scale semantic scene reconstruction
V. Vineet, O. Miksik, M. Lidegaard, M. Nießner, S. Golodetz, V. A. Prisacariu, O. Kähler, D. W. Murray, S. Izadi, P. Perez, and P. H. S. Torr · 2015
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Towards unified depth and semantic prediction from a single image
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REMODE: Probabilistic, monocular dense reconstruction in real time
M. Pizzoli, C. Forster, and D. Scaramuzza · 2014
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Real-time large scale dense RGB-D SLAM with volumetric fusion
T. Whelan, M. Kaess, H. Johannsson, M. Fallon, J. J. Leonard, and J. Mcdonald · 2014
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Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
D. Eigen and R. Fergus · 2015
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Depth and surface normal estimation from monocular images using regression on deep features and hierarchical CRFs
B. Li, C. Shen, Y. Dai, A. V. den Hengel, and M. He · 2015
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P. Wang, X. Shen, Z. Lin, S. Cohen, B. Price, and A. L. Yuille · 2015
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Real-time monocular object slam
D. Gálvez-López, M. Salas, J. D. Tardós, and J. Montiel · 2016
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Multi-level mapping: Real-time dense monocular slam
W. N. Greene, K. Ok, P. Lommel, and N. Roy · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Deeper depth prediction with fully convolutional residual networks
I. Laina, C. Rupprecht, V. Belagiannis, F. Tombari, and N. Navab · 2016
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