P. Achlioptas, O. Diamanti, I. Mitliagkas, and L. Guibas, Learning representations and generative models for 3D point clouds, 2018.

J. Behrmann, W. Grathwohl, R. Chen, D. Duvenaud, and J. H. Jacobsen, Invertible residual networks. In: ICML, 2019.

A. Bhattacharyya, M. Hanselmann, M. Fritz, B. Schiele, and C. N. Straehle, Conditional flow variational autoencoders for structured sequence prediction, NeurIPS Workshop on Machine Learning for Autonomous Driving, 2019.

C. Bishop, Pattern recognition and machine learning, 2006.

A. Brock, T. Lim, J. Ritchie, and N. Weston, Generative and discriminative voxel modeling with convolutional neural networks, NeurIPS 3D deep learning workshop, 2016.

J. R. Chang and Y. S. Chen, Batch-normalized maxout network in network, ICML, 2016.

T. Chen, Y. Rubanova, J. Bettencourt, and D. Duvenaud, Neural ordinary differential equations, 2018.

X. Chen, D. Kingma, T. Salimans, Y. Duan, P. Dhariwal et al., Variational lossy autoencoder, 2017.

C. Choy, D. Xu, J. Y. Gwak, K. Chen, and S. Savarese, 3D-R2N2: A unified approach for single and multi-view 3D object reconstruction, 2016.

G. Deco and W. Brauer, Higher order statistical decorrelation without information loss, 1995.

L. Dinh, J. Sohl-dickstein, and S. Bengio, Density estimation using Real NVP, 2017.

H. Fan, H. Su, and L. Guibas, A point set generation network for 3D object reconstruction from a single image, 2017.

R. Girdhar, D. Fouhey, M. Rodriguez, and A. Gupta, Learning a predictable and generative vector representation for objects, 2016.

I. Goodfellow, J. Pouget-abadie, M. Mirza, B. Xu, D. Warde-farley et al., Generative adversarial nets, 2014.

B. Graham, M. Engelcke, and L. Van-der-maaten, 3D semantic segmentation with submanifold sparse convolutional networks, 2018.

W. Grathwohl, R. Chen, J. Bettencourt, I. Sutskever, and D. Duvenaud, FFJORD: Free-form continuous dynamics for scalable reversible generative models, 2019.

T. Groueix, M. Fisher, V. Kim, B. Russell, and M. Aubry, A papier-mâché approach to learning 3D surface generation, 2018.

K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, 2016.

K. He, X. Zhang, S. Ren, and J. Sun, Identity mappings in deep residual networks, 2016.

P. Henderson and V. Ferrari, Learning to generate and reconstruct 3D meshes with only 2D supervision, 2018.

E. Insafutdinov and A. Dosovitskiy, Unsupervised learning of shape and pose with differentiable point clouds, 2018.

T. Karras, T. Aila, S. Laine, and J. Lehtinen, Progressive growing of GANSs for improved quality, stability, and variation, 2018.

D. Kingma and P. Dhariwal, Glow: Generative flow with invertible 1×1 convolutions, 2018.

D. Kingma, T. Salimans, R. Jozefowicz, X. Chen, I. Sutskever et al., Improved variational inference with inverse autoregressive flow, 2016.

D. Kingma and M. Welling, Auto-encoding variational Bayes, 2014.

R. Klokov and V. Lempitsky, Escape from cells: Deep Kd-networks for the recognition of 3D point cloud models, 2017.

R. Klokov, J. Verbeek, and E. Boyer, Probabilistic reconstruction networks for 3D shape inference from a single image, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02268466

I. Kobyzev, S. Prince, and M. Brubaker, Normalizing flows: An introduction and review of current methods, 2019.

C. L. Li, M. Zaheer, Y. Zhang, B. Poczos, and R. Salakhutdinov, , 2018.

I. Loshchilov and F. Hutter, Decoupled weight decay regularization, 2019.

Y. Lu and B. Huang, Structured output learning with conditional generative flows, 2020.

T. Lucas, K. Shmelkov, K. Alahari, C. Schmid, and J. Verbeek, Adaptive density estimation for generative models, 2019.
URL : https://hal.archives-ouvertes.fr/hal-01886285

P. Mandikal, K. Navaneet, M. Agarwal, and R. Babu, 3D-LMNet: Latent embedding matching for accurate and diverse 3D point cloud reconstruction from a single image, 2018.

D. Maturana and S. Scherer, VoxNet: A 3D convolutional neural network for real-time object recognition, 2015.

M. Michalkiewicz, J. Pontes, D. Jack, M. Baktashmotlagh, and A. Eriksson, Implicit surface representations as layers in neural networks. In: ICCV, 2019.

F. Monti, D. Boscaini, J. Masci, E. Rodolà, J. Svoboda et al., Geometric deep learning on graphs and manifolds using mixture model CNNs, 2017.

G. Papamakarios, E. Nalisnick, D. Rezende, S. Mohamed, and B. Lakshminarayanan, Normalizing flows for probabilistic modeling and inference, 2019.

J. Park, P. Florence, J. Straub, R. Newcombe, and S. Lovegrove, DeepSDF: Learning continuous signed distance functions for shape representation, 2019.

E. Perez, F. Strub, H. D. Vries, V. Dumoulin, and A. Courville, FiLM: Visual reasoning with a general conditioning layer, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01648685

A. Pumarola, S. Popov, F. Moreno-noguer, and V. Ferrari, C-Flow: Conditional generative flow models for images and 3D point clouds, 2020.

C. Qi, H. Su, K. Mo, and L. Guibas, Pointnet: Deep learning on point sets for 3D classification and segmentation, 2017.

C. Qi, L. Yi, H. Su, and L. Guibas, Pointnet++: Deep hierarchical feature learning on point sets in a metric space, 2017.

S. Reddi, S. Kale, and S. Kumar, On the convergence of Adam and beyond, 2018.

D. Rezende and S. Mohamed, Variational inference with normalizing flows, ICML, 2015.

D. Rezende, S. Mohamed, and D. Wierstra, Stochastic backpropagation and approximate inference in deep generative models, 2014.

G. Riegler, A. Ulusoy, and A. Geiger, OctNet: Learning deep 3D representations at high resolutions, 2017.

H. Su, V. Jampani, D. Sun, S. Maji, E. Kalogerakis et al., SPLATNet: Sparse lattice networks for point cloud processing, 2018.

M. Tatarchenko, A. Dosovitskiy, and T. Brox, Octree generating networks: Efficient convolutional architectures for high-resolution 3D outputs, 2017.

N. Verma, E. Boyer, and J. Verbeek, FeaStNet: Feature-steered graph convolutions for 3D shape analysis, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01540389

K. Wang, K. Chen, and K. Jia, Deep cascade generation on point sets, IJCAI, 2019.

N. Wang, Y. Zhang, Z. Li, Y. Fu, W. Liu et al., Pixel2Mesh: Generating 3D mesh models from single RGB images, 2018.

J. Wu, C. Zhang, T. Xue, W. Freeman, and J. Tenenbaum, Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling, 2016.

Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang et al., 3D ShapeNets: A deep representation for volumetric shapes, 2015.

G. Yang, X. Huang, Z. Hao, M. Y. Liu, S. Belongie et al., PointFlow: 3D point cloud generation with continuous normalizing flows, 2019.

M. Zaheer, S. Kottur, S. Ravanbakhsh, B. Poczos, R. Salakhutdinov et al., Deep sets, 2017.