Fetching the paper…
Reading the bibliography…
We present a framework for learning single-view shape and pose prediction without using direct supervision for either.
The ecological approach to visual perception
J. J. Gibson · 1979
Earlier work this paper cites.
The interpretation of structure from motion
S. Ullman · 1979
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
R. J. Williams · 1992
Earlier work this paper cites.
The visual hull concept for silhouette-based image understanding
A. Laurentini · 1994
Earlier work this paper cites.
A volumetric method for building complex models from range images
B. Curless and M. Levoy · 1996
Earlier work this paper cites.
A morphable model for the synthesis of 3d faces
V. Blanz and T. Vetter · 1999
Earlier work this paper cites.
Roxels: Responsibility weighted 3d volume reconstruction
J. De Bonet and P. Viola · 1999
Earlier work this paper cites.
Image-based visual hulls
W. Matusik, C. Buehler, R. Raskar, S. J. Gortler, and L. McMillan · 2000
Earlier work this paper cites.
A probabilistic framework for space carving
A. Broadhurst, T. W. Drummond, and R. Cipolla · 2001
Earlier work this paper cites.
Unsupervised 3d object recognition and reconstruction in unordered datasets
M. Brown and D. G. Lowe · 2005
Earlier work this paper cites.
Photo tourism: exploring photo collections in 3d
N. Snavely, S. M. Seitz, and R. Szeliski · 2006
Earlier work this paper cites.
Ray markov random fields for image-based 3d modeling: model and efficient inference
S. Liu and D. B. Cooper · 2010
Earlier work this paper cites.
What shape are dolphins? building 3d morphable models from 2d images
T. J. Cashman and A. W. Fitzgibbon · 2013
Cited alongside, same era.
Joint semantic segmentation and 3d reconstruction from monocular video
A. Kundu, Y. Li, F. Dellaert, F. Li, and J. M. Rehg · 2014
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 · 2015
Cited alongside, same era.
Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
D. Eigen and R. Fergus · 2015
Cited alongside, same era.
Category-specific object reconstruction from a single image
A. Kar, S. Tulsiani, J. Carreira, and J. Malik · 2015
Cited alongside, same era.
Semantic 3d reconstruction with continuous regularization and ray potentials using a visibility consistency constraint
N. Savinov, C. Hane, L. Ladicky, and M. Pollefeys · 2016
Later among the works it cites.
Deep metric learning via lifted structured feature embedding
H. O. Song, Y. Xiang, S. Jegelka, and S. Savarese · 2016
Later among the works it cites.
Perspective transformer nets: Learning single-view 3d object reconstruction without 3d supervision
X. Yan, J. Yang, E. Yumer, Y. Guo, and H. Lee · 2016
Later among the works it cites.
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2017
Later among the works it cites.
Unsupervised 3d shape induction from 2d views of multiple objects
M. Gadelha, S. Maji, and R. Wang · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Discrete optimization of ray potentials for semantic 3d reconstruction
N. Savinov, C. Häne, M. Pollefeys, et al · 2015
Cited alongside, same era.
Towards probabilistic volumetric reconstruction using ray potentials
A. O. Ulusoy, A. Geiger, and M. J. Black · 2015
Cited alongside, same era.
3d-r2n2: A unified approach for single and multi-view 3d object reconstruction
C. B. Choy, D. Xu, J. Gwak, K. Chen, and S. Savarese · 2016
Cited alongside, same era.
Attend, infer, repeat: Fast scene understanding with generative models
S. A. Eslami, N. Heess, T. Weber, Y. Tassa, D. Szepesvari, G. E. Hinton, et al · 2016
Cited alongside, same era.
Unsupervised cnn for single view depth estimation: Geometry to the rescue
R. Garg and I. Reid · 2016
Cited alongside, same era.
Learning a predictable and generative vector representation for objects
R. Girdhar, D. Fouhey, M. Rodriguez, and A. Gupta · 2016
Cited alongside, same era.
Unsupervised learning of 3d structure from images
D. J. Rezende, S. A. Eslami, S. Mohamed, P. Battaglia, M. Jaderberg, and N. Heess · 2016
Cited alongside, same era.
Unsupervised monocular depth estimation with left-right consistency
C. Godard, O. Mac Aodha, and G. J. Brostow · 2017
Later among the works it cites.
Weakly supervised 3d reconstruction with adversarial constraint
J. Gwak, C. B. Choy, A. Garg, M. Chandraker, and S. Savarese · 2017
Later among the works it cites.
Multi-view supervision for single-view reconstruction via differentiable ray consistency
S. Tulsiani, T. Zhou, A. A. Efros, and J. Malik · 2017
Later among the works it cites.
MarrNet: 3D Shape Reconstruction via 2.5D Sketches
J. Wu, Y. Wang, T. Xue, X. Sun, W. T. Freeman, and J. B. Tenenbaum · 2017
Later among the works it cites.
Unsupervised learning of depth and ego-motion from video
T. Zhou, M. Brown, N. Snavely, and D. Lowe · 2017
Later among the works it cites.
Rethinking reprojection: Closing the loop for pose-aware shape reconstruction from a single image
R. Zhu, H. Kiani, C. Wang, and S. Lucey · 2017
Later among the works it cites.