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3D-Machine-Learning
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Yuxuan (Tim) Zhang 2017-08-15 01:19:34 -04:00 committed by GitHub
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@ -24,17 +24,67 @@ To make it a collaborative project, you may add content throught pull requests o
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/).
## Single Object Classification
to be added
:space_invader: <b>3D ShapeNets: A Deep Representation for Volumetric Shapes (2015)</b> [[Paper]](http://3dshapenets.cs.princeton.edu/)
<p align="center"><img width="50%" src="https://ai2-s2-public.s3.amazonaws.com/figures/2016-11-08/3ed23386284a5639cb3e8baaecf496caa766e335/1-Figure1-1.png" /></p>
:space_invader: <b>VoxNet: A 3D Convolutional Neural Network for Real-Time Object Recognition (2015)</b> [[Paper]](http://www.dimatura.net/publications/voxnet_maturana_scherer_iros15.pdf) [[Code]](https://github.com/dimatura/voxnet)
<p align="center"><img width="50%" src="http://www.dimatura.net/research/voxnet/car_voxnet_side.png" /></p>
:camera: <b>Multi-view Convolutional Neural Networks for 3D Shape Recognition (2015)</b> [[Paper]](http://vis-www.cs.umass.edu/mvcnn/)
<p align="center"><img width="50%" src="http://vis-www.cs.umass.edu/mvcnn/images/mvcnn.png" /></p>
:camera: <b>DeepPano: Deep Panoramic Representation for 3-D Shape Recognition (2015)</b> [[Paper]](http://mclab.eic.hust.edu.cn/UpLoadFiles/Papers/DeepPano_SPL2015.pdf)
<p align="center"><img width="30%" src="https://ai2-s2-public.s3.amazonaws.com/figures/2016-11-08/5a1b5d31905d8cece7b78510f51f3d8bbb063063/1-Figure3-1.png" /></p>
:space_invader::camera: <b>FusionNet: 3D Object Classification Using Multiple Data Representations (2016)</b> [[Paper]](https://stanford.edu/~rezab/papers/fusionnet.pdf)
<p align="center"><img width="30%" src="https://ai2-s2-public.s3.amazonaws.com/figures/2016-11-08/0aab8fbcef1f0a14f5653d170ca36f4e5aae8010/6-Figure5-1.png" /></p>
:space_invader::camera: <b>Volumetric and Multi-View CNNs for Object Classification on 3D Data (2016)</b> [[Paper]](https://arxiv.org/pdf/1604.03265.pdf) [[Code]](https://github.com/charlesq34/3dcnn.torch)
<p align="center"><img width="40%" src="http://graphics.stanford.edu/projects/3dcnn/teaser.jpg" /></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>
: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>
: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>
:space_invader: <b>OctNet: Learning Deep 3D Representations at High Resolutions (2017)</b> [[Paper]](https://github.com/griegler/octnet)
<p align="center"><img width="30%" src="https://is.tuebingen.mpg.de/uploads/publication/image/18921/img03.png" /></p>
:space_invader: <b>O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis (2017)</b> [[Paper]](http://wang-ps.github.io/O-CNN)
<p align="center"><img width="50%" src="http://wang-ps.github.io/O-CNN_files/teaser.png" /></p>
:game_die: <b>PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation (2017)</b> [[Paper]](http://stanford.edu/~rqi/pointnet/)
<p align="center"><img width="40%" src="https://web.stanford.edu/~rqi/papers/pointnet.png" /></p>
:game_die: <b>PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space (2017)</b> [[Paper]](https://arxiv.org/pdf/1706.02413.pdf)
<p align="center"><img width="40%" src="https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/PointNet%2B%2B-%20Deep%20Hierarchical%20Feature%20Learning%20on%20Point%20Sets%20in%20a%20Metric%20Space.png" /></p>
:camera: <b>Feedback Networks (2017)</b> [[Paper]](http://feedbacknet.stanford.edu/)
<p align="center"><img width="50%" src="https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Feedback%20Networks.png" /></p>
## Multiple Objects Detection
to be added
<b>Sliding Shapes for 3D Object Detection in Depth Images (2014)</b> [[Paper]](http://slidingshapes.cs.princeton.edu/)
<p align="center"><img width="50%" src="http://slidingshapes.cs.princeton.edu/teaser.jpg" /></p>
<b>Object Detection in 3D Scenes Using CNNs in Multi-view Images (2016)</b> [[Paper]](https://stanford.edu/class/ee367/Winter2016/Qi_Report.pdf)
<p align="center"><img width="50%" src="https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Object%20Detection%20in%203D%20Scenes%20Using%20CNNs%20in%20Multi-view%20Images.png" /></p>
<b>Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images (2016)</b> [[Paper]](http://dss.cs.princeton.edu/)
<p align="center"><img width="50%" src="http://3dvision.princeton.edu/slide/DSS.jpg" /></p>
<b>DeepContext: Context-Encoding Neural Pathways for 3D Holistic Scene Understanding (2016)</b> [[Paper]](http://deepcontext.cs.princeton.edu/)
<p align="center"><img width="50%" src="http://deepcontext.cs.princeton.edu/teaser.png" /></p>
## Part Segmentation
<b>Learning 3D Mesh Segmentation and Labeling (2010)</b> [[Paper]](https://people.cs.umass.edu/~kalo/papers/LabelMeshes/LabelMeshes.pdf)
<p align="center"><img width="50%" src="https://ai2-s2-public.s3.amazonaws.com/figures/2016-11-08/0bf390e2a14f74bcc8838d5fb1c0c4cc60e92eb7/7-Figure7-1.png" /></p>
<b>Unsupervised Co-Segmentation of a Set of Shapes via Descriptor-Space Spectral Clustering (2011)</b> [[Paper]](https://www.cs.sfu.ca/~haoz/pubs/sidi_siga11_coseg.pdf)
<p align="center"><img width="50%" src="http://people.scs.carleton.ca/~olivervankaick/cosegmentation/results6.png" /></p>
<p align="center"><img width="30%" src="http://people.scs.carleton.ca/~olivervankaick/cosegmentation/results6.png" /></p>
<b>3D Shape Segmentation with Projective Convolutional Networks (2017)</b> [[Paper]](http://people.cs.umass.edu/~kalo/papers/shapepfcn/)
<p align="center"><img width="50%" src="http://people.cs.umass.edu/~kalo/papers/shapepfcn/teaser.jpg" /></p>
@ -42,6 +92,12 @@ to be added
<b>Learning Hierarchical Shape Segmentation and Labeling from Online Repositories (2017)</b> [[Paper]](http://cs.stanford.edu/~ericyi/project_page/hier_seg/index.html)
<p align="center"><img width="50%" src="http://cs.stanford.edu/~ericyi/project_page/hier_seg/figures/teaser.jpg" /></p>
:game_die: <b>PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation (2017)</b> [[Paper]](http://stanford.edu/~rqi/pointnet/)
<p align="center"><img width="50%" src="https://web.stanford.edu/~rqi/papers/pointnet.png" /></p>
:game_die: <b>PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space (2017)</b> [[Paper]](https://arxiv.org/pdf/1706.02413.pdf)
<p align="center"><img width="50%" src="https://pbs.twimg.com/media/DB-i3yRUQAARc6g.png" /></p>
## 3D Synthesis/Reconstruction
_Parametric Morphable Model-based methods_