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We introduce SceneNet RGB-D, expanding the previous work of SceneNet to enable large scale photorealistic rendering of indoor scene trajectories.
A practical guide to global illumination using photon maps
H. W. Jensen and N. J. Christensen · 2000
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Example-based synthesis of 3d object arrangements
M. Fisher, D. Ritchie, M. Savva, T. Funkhouser, and P. Hanrahan · 2012
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Real-Time Camera Tracking: When is High Frame-Rate Best?
A. Handa, R. A. Newcombe, A. Angeli, and A. J. Davison · 2012
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. Hinton · 2012
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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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Progressive photon mapping on gpus
S. A. Pedersen · 2013
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Seeing 3D chairs: exemplar part-based 2D-3D alignment using a large dataset of CAD models
M. Aubry, D. Maturana, A. Efros, B. Russell, and J. Sivic · 2014
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A Benchmark for RGB-D Visual Odometry, 3D Reconstruction and SLAM
A. Handa, T. Whelan, J. B. McDonald, and A. J. Davison · 2014
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Learning Deep Object Detectors from 3D Models
X. Peng, B. Sun, K. Ali, and K. Saenko · 2014
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On being the right scale: Sizing large collections of 3D models
M. Savva, A. X. Chang, G. Bernstein, C. D. Manning, and P. Hanrahan · 2014
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FlowNet: Learning Optical Flow with Convolutional Networks
P. Fischer, A. Dosovitskiy, E. Ilg, P. Häusser, C. Hazırbaş, V. Golkov, P. van der Smagt, D. Cremers, and T. Brox · 2015
Cited alongside, same era.
Aligning 3D models to RGB-D images of cluttered scenes
S. Gupta, P. A. Arbeláez, R. B. Girshick, and J. Malik · 2015
Cited alongside, same era.
SceneNet: Understanding Real World Indoor Scenes With Synthetic Data
A. Handa, V. Pătrăucean, V. Badrinarayanan, S. Stent, and R. Cipolla · 2015
Cited alongside, same era.
SUN RGB-D: A RGB-D scene understanding benchmark suite
Scenenn: A scene meshes dataset with annotations
B.-S. Hua, Q.-H. Pham, D. T. Nguyen, M.-K. Tran, L.-F. Yu, and S.-K. Yeung · 2016
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Flownet 2.0: Evolution of optical flow estimation with deep networks
E. Ilg, N. Mayer, T. Saikia, M. Keuper, A. Dosovitskiy, and T. Brox · 2016
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SemanticFusion: Dense 3D Semantic Mapping with Convolutional Neural Networks
J. McCormac, A. Handa, A. J. Davison, and S. Leutenegger · 2016
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UnrealCV: Connecting computer vision to unreal engine
W. Qiu and A. Yuille · 2016
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Playing for data: Ground truth from computer games
S. R. Richter, V. Vineet, S. Roth, and V. Koltun · 2016
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S. Song, S. P. Lichtenberg, and J. Xiao · 2015
Cited alongside, same era.
Domain Separation Networks
K. Bousmalis, G. Trigeorgis, N. Silberman, D. Krishnan, and D. Erhan · 2016
Cited alongside, same era.
Procedural Generation of Videos to Train Deep Action Recognition Networks
C. R. de Souza, A. Gaidon, Y. Cabon, and A. M. López Peña · 2016
Cited alongside, same era.
ShapeNet: An Information-Rich 3D Model Repository
A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, J. Xiao, L. Yi, and F. Yu
Cited in the paper.
G. Ros, L. Sellart, J. Materzynska, D. Vazquez, and A. Lopez · 2016
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Semantic Scene Completion from a Single Depth Image
S. Song, F. Yu, A. Zeng, A. X. Chang, M. Savva, and T. Funkhouser · 2016
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