Fetching the paper…
Reading the bibliography…
We present a probabilistic 3D generative model, named Generative Cellular Automata, which is able to produce diverse and high quality shapes.
Cellular automata as simple self-organizing systems
Stephen Wolfram · 1982
Earlier work this paper cites.
Isotropic cellular automaton for modelling excitable media
Mario Markus and Benno Hess · 1990
Earlier work this paper cites.
Self-improving reactive agents based on reinforcement learning, planning and teaching
Long ji Lin · 1992
Earlier work this paper cites.
Learning cellular automaton dynamics with neural networks
N. H. Wulff and J. A. Hertz · 1992
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Scheduled sampling for sequence prediction with recurrent neural networks
Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer · 2015
Earlier work this paper cites.
ShapeNet: An Information-Rich 3D Model Repository
Angel X. Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, and Fisher Yu · 2015
Earlier work this paper cites.
How (not) to train your generative model: Scheduled sampling, likelihood, adversary?, 2015
Ferenc Huszár · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederick P Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P.Fischer, and T. Brox · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Earlier work this paper cites.
Revisiting Classifier Two-Sample Tests
David Lopez-Paz and Maxime Oquab · 2016
Cited alongside, same era.
Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2016
Cited alongside, same era.
Conditional image generation with pixelcnn decoders
Aaron van den Oord, Nal Kalchbrenner, Lasse Espeholt, koray kavukcuoglu, Oriol Vinyals, and Alex Graves · 2016
Cited alongside, same era.
Pixel rnn
Aäron van den Oord and Nal Kalchbrenner · 2016
Cited alongside, same era.
Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling
Jiajun Wu, Chengkai Zhang, Tianfan Xue, William T. Freeman, and Joshua B. Tenenbaum · 2016
Cited alongside, same era.
Learning representations and generative models for 3D point clouds
Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas Guibas · 2018
Later among the works it cites.
3d semantic segmentation with submanifold sparse convolutional networks
Benjamin Graham, Martin Engelcke, and Laurens van der Maaten · 2018
Later among the works it cites.
4d spatio-temporal convnets: Minkowski convolutional neural networks
Christopher Choy, JunYoung Gwak, and Silvio Savarese · 2019
Later among the works it cites.
PartNet: A large-scale benchmark for fine-grained and hierarchical part-level 3D object understanding
Kaichun Mo, Shilin Zhu, Angel X. Chang, Li Yi, Subarna Tripathi, Leonidas J. Guibas, and Hao Su · 2019
Later among the works it cites.
3d point cloud generative adversarial network based on tree structured graph convolutions
Dong Wook Shu, Sung Woo Park, and Junseok Kwon · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Variational walkback: Learning a transition operator as a stochastic recurrent net
Anirudh, Nan Rosemary Ke, Surya Ganguli, and Yoshua Bengio · 2017
Cited alongside, same era.
Learning to generate samples from noise through infusion training
Florian Bordes, Sina Honari, and Pascal Vincent · 2017
Cited alongside, same era.
Shape completion using 3d-encoder-predictor cnns and shape synthesis
Angela Dai, Charles Ruizhongtai Qi, and Matthias Nießner · 2017
Cited alongside, same era.
Mask r-cnn
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
Cited alongside, same era.
Improved adversarial systems for 3d object generation and reconstruction
Edward J. Smith and David Meger · 2017
Cited alongside, same era.
Neural discrete representation learning
Aaron van den Oord, Oriol Vinyals, and Koray Kavukcuoglu · 2017
Cited alongside, same era.
Learning localized representations of point clouds with graph-convolutional generative adversarial networks
Diego Valsesia, Giulia Fracastoro, and Enrico Magli · 2019
Later among the works it cites.
Pointflow: 3d point cloud generation with continuous normalizing flows
Guandao Yang, Xun Huang, Zekun Hao, Ming-Yu Liu, Serge Belongie, and Bharath Hariharan · 2019
Later among the works it cites.
Learning gradient fields for shape generation
Ruojin Cai, Guandao Yang, Hadar Averbuch-Elor, Zekun Hao, Serge Belongie, Noah Snavely, and Bharath Hariharan · 2020
Later among the works it cites.
Thread: Differentiable self-organizing systems
Alexander Mordvintsev, Ettore Randazzo, Eyvind Niklasson, Michael Levin, and Sam Greydanus · 2020
Later among the works it cites.
Pointgrow: Autoregressively learned point cloud generation with self-attention
Yongbin Sun, Yue Wang, Ziwei Liu, Joshua Siegel, and Sanjay Sarma · 2020
Later among the works it cites.
Multimodal shape completion via conditional generative adversarial networks, 2020
Rundi Wu, Xuelin Chen, Yixin Zhuang, and Baoquan Chen · 2020
Later among the works it cites.