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@ -20,6 +20,8 @@ To make it a collaborative project, you may add content throught pull requests o
[Princeton CS597: Geometric Modeling and Analysis (Fall 2003)](https://www.cs.princeton.edu/courses/archive/fall03/cs597D/) [Princeton CS597: Geometric Modeling and Analysis (Fall 2003)](https://www.cs.princeton.edu/courses/archive/fall03/cs597D/)
[Geometric Deep Learning](http://geometricdeeplearning.com/)
## Datasets ## Datasets
To see a survey of RGBD datasets, I recommend to check out Michael Firman's [collection](http://www0.cs.ucl.ac.uk/staff/M.Firman//RGBDdatasets/) as well as the associated paper, [RGBD Datasets: Past, Present and Future](https://arxiv.org/pdf/1604.00999.pdf). Point Cloud Library also has a good dataset [catalogue](http://pointclouds.org/media/). To see a survey of RGBD datasets, I recommend to check out Michael Firman's [collection](http://www0.cs.ucl.ac.uk/staff/M.Firman//RGBDdatasets/) as well as the associated paper, [RGBD Datasets: Past, Present and Future](https://arxiv.org/pdf/1604.00999.pdf). Point Cloud Library also has a good dataset [catalogue](http://pointclouds.org/media/).
@ -48,6 +50,9 @@ To see a survey of RGBD datasets, I recommend to check out Michael Firman's [col
:space_invader: <b>3D GAN: Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling (2016)</b> [[Paper]](https://arxiv.org/pdf/1610.07584.pdf) :space_invader: <b>3D GAN: Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling (2016)</b> [[Paper]](https://arxiv.org/pdf/1610.07584.pdf)
<p align="center"><img width="50%" src="http://3dgan.csail.mit.edu/images/model.jpg" /></p> <p align="center"><img width="50%" src="http://3dgan.csail.mit.edu/images/model.jpg" /></p>
:space_invader: <b>Generative and Discriminative Voxel Modeling with Convolutional Neural Networks (2017)</b> [[Paper]](https://github.com/ajbrock/Generative-and-Discriminative-Voxel-Modeling)
<p align="center"><img width="50%" src="https://github.com/ajbrock/Generative-and-Discriminative-Voxel-Modeling/blob/master/doc/GUI3.png" /></p>
:space_invader: <b>FPNN: Field Probing Neural Networks for 3D Data (2016)</b> [[Paper]](http://yangyanli.github.io/FPNN/) :space_invader: <b>FPNN: Field Probing Neural Networks for 3D Data (2016)</b> [[Paper]](http://yangyanli.github.io/FPNN/)
<p align="center"><img width="30%" src="https://ai2-s2-public.s3.amazonaws.com/figures/2016-11-08/15ca7adccf5cd4dc309cdcaa6328f4c429ead337/1-Figure2-1.png" /></p> <p align="center"><img width="30%" src="https://ai2-s2-public.s3.amazonaws.com/figures/2016-11-08/15ca7adccf5cd4dc309cdcaa6328f4c429ead337/1-Figure2-1.png" /></p>
@ -159,6 +164,9 @@ _Deep Learning Methods_
:camera: <b>Learning to Generate Chairs, Tables and Cars with Convolutional Networks (2014)</b> [[Paper]](https://arxiv.org/pdf/1411.5928.pdf) :camera: <b>Learning to Generate Chairs, Tables and Cars with Convolutional Networks (2014)</b> [[Paper]](https://arxiv.org/pdf/1411.5928.pdf)
<p align="center"><img width="50%" src="https://zo7.github.io/img/2016-09-25-generating-faces/chairs-model.png" /></p> <p align="center"><img width="50%" src="https://zo7.github.io/img/2016-09-25-generating-faces/chairs-model.png" /></p>
:camera: <b>Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis (2015, NIPS)</b> [[Paper]](https://papers.nips.cc/paper/5639-weakly-supervised-disentangling-with-recurrent-transformations-for-3d-view-synthesis.pdf)
<p align="center"><img width="50%" src="https://github.com/jimeiyang/deepRotator/blob/master/demo_img.png" /></p>
:game_die: <b>Analysis and synthesis of 3D shape families via deep-learned generative models of surfaces (2015)</b> [[Paper]](https://people.cs.umass.edu/~hbhuang/publications/bsm/) :game_die: <b>Analysis and synthesis of 3D shape families via deep-learned generative models of surfaces (2015)</b> [[Paper]](https://people.cs.umass.edu/~hbhuang/publications/bsm/)
<p align="center"><img width="50%" src="https://people.cs.umass.edu/~hbhuang/publications/bsm/bsm_teaser.jpg" /></p> <p align="center"><img width="50%" src="https://people.cs.umass.edu/~hbhuang/publications/bsm/bsm_teaser.jpg" /></p>
@ -189,6 +197,9 @@ _Deep Learning Methods_
:camera: <b>Unsupervised Learning of 3D Structure from Images (2016)</b> [[Paper]](https://arxiv.org/pdf/1607.00662.pdf) :camera: <b>Unsupervised Learning of 3D Structure from Images (2016)</b> [[Paper]](https://arxiv.org/pdf/1607.00662.pdf)
<p align="center"><img width="50%" src="https://adriancolyer.files.wordpress.com/2016/12/unsupervised-3d-fig-10.jpeg?w=600" /></p> <p align="center"><img width="50%" src="https://adriancolyer.files.wordpress.com/2016/12/unsupervised-3d-fig-10.jpeg?w=600" /></p>
:space_invader: <b>Generative and Discriminative Voxel Modeling with Convolutional Neural Networks (2016)</b> [[Paper]](https://arxiv.org/pdf/1608.04236.pdf) [[Code]](https://github.com/ajbrock/Generative-and-Discriminative-Voxel-Modeling)
<p align="center"><img width="50%" src="http://davidstutz.de/wordpress/wp-content/uploads/2017/02/brock_vae.png" /></p>
:camera: <b>Multi-view Supervision for Single-view Reconstruction via Differentiable Ray Consistency (2017)</b> [[Paper]](https://shubhtuls.github.io/drc/) :camera: <b>Multi-view Supervision for Single-view Reconstruction via Differentiable Ray Consistency (2017)</b> [[Paper]](https://shubhtuls.github.io/drc/)
<p align="center"><img width="50%" src="https://shubhtuls.github.io/drc/resources/images/teaserChair.png" /></p> <p align="center"><img width="50%" src="https://shubhtuls.github.io/drc/resources/images/teaserChair.png" /></p>
@ -198,15 +209,36 @@ _Deep Learning Methods_
:space_invader: <b>Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs (2017)</b> [[Paper]](https://arxiv.org/pdf/1703.09438.pdf) :space_invader: <b>Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs (2017)</b> [[Paper]](https://arxiv.org/pdf/1703.09438.pdf)
<p align="center"><img width="50%" src="https://ai2-s2-public.s3.amazonaws.com/figures/2016-11-08/6c2a292bb018a8742cbb0bbc5e23dd0a454ffe3a/2-Figure2-1.png" /></p> <p align="center"><img width="50%" src="https://ai2-s2-public.s3.amazonaws.com/figures/2016-11-08/6c2a292bb018a8742cbb0bbc5e23dd0a454ffe3a/2-Figure2-1.png" /></p>
:space_invader: <b>Hierarchical Surface Prediction for 3D Object Reconstruction (2017)</b> [[Paper]](https://arxiv.org/pdf/1704.00710.pdf)
<p align="center"><img width="50%" src="http://bair.berkeley.edu/blog/assets/hsp/image_2.png" /></p>
:game_die: <b>A Point Set Generation Network for 3D Object Reconstruction from a Single Image (2017)</b> [[Paper]](http://ai.stanford.edu/~haosu/papers/SI2PC_arxiv_submit.pdf) :game_die: <b>A Point Set Generation Network for 3D Object Reconstruction from a Single Image (2017)</b> [[Paper]](http://ai.stanford.edu/~haosu/papers/SI2PC_arxiv_submit.pdf)
<p align="center"><img width="50%" src="http://gting.me/2017/07/17/pr-point-set-generation-from-single-image/ps3d_introduction.PNG" /></p> <p align="center"><img width="50%" src="http://gting.me/2017/07/17/pr-point-set-generation-from-single-image/ps3d_introduction.PNG" /></p>
:game_die: <b>DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Image (2017)</b> [[Paper]](http://ai.stanford.edu/~haosu/papers/SI2PC_arxiv_submit.pdf)
<p align="center"><img width="50%" src="https://chrischoy.github.io/images/publication/deformnet/model.png" /></p>
:camera: <b>Transformation-Grounded Image Generation Network for Novel 3D View Synthesis (2017)</b> [[Paper]](http://www.cs.unc.edu/~eunbyung/tvsn/) :camera: <b>Transformation-Grounded Image Generation Network for Novel 3D View Synthesis (2017)</b> [[Paper]](http://www.cs.unc.edu/~eunbyung/tvsn/)
<p align="center"><img width="50%" src="https://eng.ucmerced.edu/people/jyang44/pics/view_synthesis.gif" /></p> <p align="center"><img width="50%" src="https://eng.ucmerced.edu/people/jyang44/pics/view_synthesis.gif" /></p>
:camera: <b>Tag Disentangled Generative Adversarial Networks for Object Image Re-rendering (2017)</b> [[Paper]](http://static.ijcai.org/proceedings-2017/0404.pdf)
<p align="center"><img width="50%" src="https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Tag%20Disentangled%20Generative%20Adversarial%20Networks%20for%20Object%20Image%20Re-rendering.jpeg" /></p>
:camera: <b>3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks (2017)</b> [[Paper]](http://www.cs.unc.edu/~eunbyung/tvsn/)
<p align="center"><img width="50%" src="https://people.cs.umass.edu/~zlun/papers/SketchModeling/SketchModeling_teaser.png" /></p>
:space_invader: <b>Interactive 3D Modeling with a Generative Adversarial Network (2017)</b> [[Paper]](https://arxiv.org/pdf/1706.05170.pdf) :space_invader: <b>Interactive 3D Modeling with a Generative Adversarial Network (2017)</b> [[Paper]](https://arxiv.org/pdf/1706.05170.pdf)
<p align="center"><img width="50%" src="https://pbs.twimg.com/media/DCsPKLqXoAEBd-V.jpg" /></p> <p align="center"><img width="50%" src="https://pbs.twimg.com/media/DCsPKLqXoAEBd-V.jpg" /></p>
:camera::space_invader: <b>Weakly supervised 3D Reconstruction with Adversarial Constraint (2017)</b> [[Paper]](https://arxiv.org/pdf/1705.10904.pdf)
<p align="center"><img width="50%" src="https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Weakly%20supervised%203D%20Reconstruction%20with%20Adversarial%20Constraint%20(2017).jpeg" /></p>
:gem: <b>Exploring Generative 3D Shapes Using Autoencoder Networks (Autodesk 2017)</b> [[Paper]](https://www.autodeskresearch.com/publications/exploring_generative_3d_shapes)
<p align="center"><img width="50%" src="https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Exploring%20Generative%203D%20Shapes%20Using%20Autoencoder%20Networks.jpeg" /></p>
:pill: <b>GRASS: Generative Recursive Autoencoders for Shape Structures (SIGGRAPH 2017)</b> [[Paper]](http://kevinkaixu.net/projects/grass.html)
<p align="center"><img width="50%" src="http://kevinkaixu.net/projects/grass/teaser.jpg" /></p>
## Style Transfer ## Style Transfer
<b>Style-Content Separation by Anisotropic Part Scales (2010)</b> [[Paper]](https://www.cs.sfu.ca/~haoz/pubs/xu_siga10_style.pdf) <b>Style-Content Separation by Anisotropic Part Scales (2010)</b> [[Paper]](https://www.cs.sfu.ca/~haoz/pubs/xu_siga10_style.pdf)
<p align="center"><img width="50%" src="https://sites.google.com/site/kevinkaixu/_/rsrc/1472852123106/publications/style_b.jpg?height=145&width=400" /></p> <p align="center"><img width="50%" src="https://sites.google.com/site/kevinkaixu/_/rsrc/1472852123106/publications/style_b.jpg?height=145&width=400" /></p>