Fetching the paper…
Reading the bibliography…
We introduce PQ-NET, a deep neural network which represents and generates 3D shapes via sequential part assembly.
Parts of recognition
D. D. Hoffman and W. A. Richards · 1984
Earlier work this paper cites.
Recognition-by-components: A theory of human image understanding
I. Biederman · 1987
Earlier work this paper cites.
Modern phrase structure grammar
R. D. Borsley · 1996
Earlier work this paper cites.
Bidirectional recurrent neural networks
M. Schuster and K. K. Paliwal · 1997
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.
The space of human body shapes: Reconstruction and parameterization from range scans
B. Allen, B. Curless, and Z. Popović · 2003
Earlier work this paper cites.
On visual similarity based 3d model retrieval
D.-Y. Chen, X.-P. Tian, Y.-T. Shen, and M. Ouhyoung · 2003
Earlier work this paper cites.
Exploration of continuous variability in collections of 3d shapes
M. Ovsjanikov, W. Li, L. Guibas, and N. J. Mitra · 2011
Earlier work this paper cites.
Symmetry hierarchy of man-made objects
Y. Wang, K. Xu, J. Li, H. Zhang, A. Shamir, L. Liu, Z. Cheng, and Y. Xiong · 2011
Earlier work this paper cites.
Does the brain do inverse graphics?
G. Hinton, A. Krizhevsky, N. Jaitly, T. Tieleman, and Y. Tang · 2012
Earlier work this paper cites.
A Probabilistic Model of Component-Based Shape Synthesis
E. Kalogerakis, S. Chaudhuri, D. Koller, and V. Koltun · 2012
Earlier work this paper cites.
Learning part-based templates from large collections of 3D shapes
V. G. Kim, W. Li, N. J. Mitra, S. Chaudhuri, S. DiVerdi, and T. Funkhouser · 2013
Earlier work this paper cites.
Learning phrase representations using RNN encoder-decoder for statistical machine translation
K. Cho, B. van Merrienboer, Ç. Gülçehre, F. Bougares, H. Schwenk, and Y. Bengio · 2014
Earlier work this paper cites.
Meta-representation of shape families
N. Fish, M. Averkiou, O. Van Kaick, O. Sorkine-Hornung, D. Cohen-Or, and N. J. Mitra · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
I. Sutskever, O. Vinyals, and Q. V. Le · 2014
Earlier work this paper cites.
ShapeNet: An Information-Rich 3D Model Repository
A. X. Chang, T. Funkhouser, L. J. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, J. Xiao, L. Yi, and F. Yu · 2015
Earlier work this paper cites.
Analysis and synthesis of 3d shape families via deep-learned generative models of surfaces
H. Huang, E. Kalogerakis, and B. Marlin · 2015
Earlier work this paper cites.
Deep convolutional inverse graphics network
T. D. Kulkarni, W. F. Whitney, P. Kohli, and J. Tenenbaum · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
A point set generation network for 3D object reconstruction from a single image
H. Fan, H. Su, and L. Guibas · 2016
Cited alongside, same era.
Learning a predictable and generative vector representation for objects
R. Girdhar, D. F. Fouhey, M. Rodriguez, and A. Gupta · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling
J. Wu, C. Zhang, T. Xue, B. Freeman, and J. Tenenbaum · 2016
Cited alongside, same era.
Wasserstein generative adversarial networks
M. Arjovsky, S. Chintala, and L. Bottou · 2017
Cited alongside, same era.
Synthesizing 3d shapes via modeling multi-view depth maps and silhouettes with deep generative networks
3d-psrnet: Part segmented 3d point cloud reconstruction from a single image
P. Mandikal, N. KL, and R. Venkatesh Babu · 2018
Later among the works it cites.
Matryoshka networks: Predicting 3d geometry via nested shape layers
S. R. Richter and S. Roth · 2018
Later among the works it cites.
Global-to-local generative model for 3d shapes
H. Wang, N. Schor, R. Hu, H. Huang, D. Cohen-Or, and H. Huang · 2018
Later among the works it cites.
Pixel2mesh: Generating 3d mesh models from single rgb images
N. Wang, Y. Zhang, Z. Li, Y. Fu, W. Liu, and Y.-G. Jiang · 2018
Later among the works it cites.
Structure-aware generative network for 3d-shape modeling
Z. Wu, X. Wang, D. Lin, D. Lischinski, D. Cohen-Or, and H. Huang · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A. Arsalan Soltani, H. Huang, J. Wu, T. D. Kulkarni, and J. B. Tenenbaum · 2017
Cited alongside, same era.
A point set generation network for 3d object reconstruction from a single image
H. Fan, H. Su, and L. J. Guibas · 2017
Cited alongside, same era.
Improved training of wasserstein gans
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. Courville · 2017
Cited alongside, same era.
Grass: Generative recursive autoencoders for shape structures
J. Li, K. Xu, S. Chaudhuri, E. Yumer, H. Zhang, and L. Guibas · 2017
Cited alongside, same era.
3d shape reconstruction from sketches via multi-view convolutional networks
Z. Lun, M. Gadelha, E. Kalogerakis, S. Maji, and R. Wang · 2017
Cited alongside, same era.
The shape variational autoencoder: A deep generative model of part-segmented 3d objects
C. Nash and C. K. Williams · 2017
Cited alongside, same era.
Octnet: Learning deep 3d representations at high resolutions
G. Riegler, A. O. Ulusoy, and A. Geiger · 2017
Cited alongside, same era.
Learning implicit fields for generative shape modeling
Z. Chen and H. Zhang · 2019
Closest in time.
Learning implicit fields for generative shape modeling
Z. Chen and H. Zhang · 2019
Closest in time.
Composite shape modeling via latent space factorization
A. Dubrovina, F. Xia, P. Achlioptas, M. Shalah, and L. Guibas · 2019
Closest in time.
Sdm-net: Deep generative network for structured deformable mesh
L. Gao, J. Yang, T. Wu, Y.-J. Yuan, H. Fu, Y.-K. Lai, and H. Zhang · 2019
Closest in time.
Probabilistic reconstruction networks for 3d shape inference from a single image
R. Klokov, J. Verbeek, and E. Boyer · 2019
Closest in time.
Learning part generation and assembly for structure-aware shape synthesis
J. Li, C. Niu, and K. Xu · 2019
Closest in time.
Point2sequence: Learning the shape representation of 3d point clouds with an attention-based sequence to sequence network
X. Liu, Z. Han, Y.-S. Liu, and M. Zwicker · 2019
Closest in time.
Occupancy networks: Learning 3D reconstruction in function space
L. Mescheder, M. Oechsle, M. Niemeyer, S. Nowozin, and A. Geiger · 2019
Closest in time.
Structurenet: Hierarchical graph networks for 3d shape generation
K. Mo, P. Guerrero, L. Yi, H. Su, P. Wonka, N. Mitra, and L. J. Guibas · 2019
Closest in time.
PartNet: A large-scale benchmark for fine-grained and hierarchical part-level 3D object understanding
K. Mo, S. Zhu, A. X. Chang, L. Yi, S. Tripathi, L. J. Guibas, and H. Su · 2019
Closest in time.
DeepSDF: Learning continuous signed distance functions for shape representation
J. J. Park, P. Florence, J. Straub, R. Newcombe, and S. Lovegrove · 2019
Closest in time.
Learning to generate the ”unseen” via part synthesis and composition
N. Schor, O. Katzier, H. Zhang, and D. Cohen-Or · 2019
Closest in time.
DISN: deep implicit surface network for high-quality single-view 3d reconstruction
Q. Xu, W. Wang, D. Ceylan, R. Mech, and U. Neumann · 2019
Closest in time.
Pointflow: 3d point cloud generation with continuous normalizing flows
G. Yang, X. Huang, Z. Hao, M.-Y. Liu, S. Belongie, and B. Hariharan · 2019
Closest in time.