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@ -472,6 +472,9 @@ _Deep Learning Methods_
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:pill: <b>GRASS: Generative Recursive Autoencoders for Shape Structures (SIGGRAPH 2017)</b> [[Paper]](http://kevinkaixu.net/projects/grass.html) [[Code]](https://github.com/junli-lj/grass) [[code]](https://github.com/kevin-kaixu/grass_pytorch)
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<p align="center"><img width="50%" src="http://kevinkaixu.net/projects/grass/teaser.jpg" /></p>
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:pill: <b> 3D-PRNN: Generating Shape Primitives with Recurrent Neural Networks (2017)</b> [[Paper]](https://arxiv.org/pdf/1708.01648.pdf)[[code]](https://github.com/zouchuhang/3D-PRNN)
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<p align="center"><img width="50%" src="https://github.com/zouchuhang/3D-PRNN/blob/master/figs/teasor.jpg" /></p>
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:gem: <b>Mesh-based Autoencoders for Localized Deformation Component Analysis (2017)</b> [[Paper]](https://arxiv.org/pdf/1709.04304.pdf)
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<p align="center"><img width="50%" src="http://qytan.com/img/point_conv.jpg" /></p>
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@ -618,3 +621,6 @@ _Deep Learning Methods_
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<b>Factoring Shape, Pose, and Layout from the 2D Image of a 3D Scene (2018 CVPR)</b> [[Paper]](http://sscnet.cs.princeton.edu/) [[Code]](https://shubhtuls.github.io/factored3d/)
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<p align="center"><img width="50%" src="https://shubhtuls.github.io/factored3d/resources/images/teaser.png" /></p>
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<b>LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image (2018 CVPR)</b> [[Paper]](https://arxiv.org/pdf/1803.08999.pdf) [[Code]](https://github.com/zouchuhang/LayoutNet)
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<p align="center"><img width="50%" src="http://p0.ifengimg.com/pmop/2018/0404/A1D0CAE48130C918FE624FA60495F237C67172F6_size63_w797_h755.jpeg" /></p>
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