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Yuxuan (Tim) Zhang 2018-01-12 00:22:20 -05:00 committed by GitHub
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@ -140,31 +140,31 @@ To see a survey of RGBD datasets, check out Michael Firman's [collection](http:/
: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)
: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) [[Code]](https://github.com/zck119/3dgan-release)
<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/) [[Code]](https://github.com/yangyanli/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)
:space_invader: <b>OctNet: Learning Deep 3D Representations at High Resolutions (2017)</b> [[Paper]](https://arxiv.org/pdf/1611.05009.pdf) [[Code]](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)
:space_invader: <b>O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis (2017)</b> [[Paper]](http://wang-ps.github.io/O-CNN) [[Code]](https://github.com/Microsoft/O-CNN)
<p align="center"><img width="50%" src="http://wang-ps.github.io/O-CNN_files/teaser.png" /></p>
:space_invader: <b>Orientation-boosted voxel nets for 3D object recognition (2017)</b> [[Paper]](https://lmb.informatik.uni-freiburg.de/Publications/2017/SZB17a/)
:space_invader: <b>Orientation-boosted voxel nets for 3D object recognition (2017)</b> [[Paper]](https://lmb.informatik.uni-freiburg.de/Publications/2017/SZB17a/) [[Code]](https://github.com/lmb-freiburg/orion)
<p align="center"><img width="50%" src="https://lmb.informatik.uni-freiburg.de/Publications/2017/SZB17a/teaser_w.png" /></p>
:game_die: <b>PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation (2017)</b> [[Paper]](http://stanford.edu/~rqi/pointnet/)
:game_die: <b>PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation (2017)</b> [[Paper]](http://stanford.edu/~rqi/pointnet/) [[Code]](https://github.com/charlesq34/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)
: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) [[Code]](https://github.com/charlesq34/pointnet2)
<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/)
:camera: <b>Feedback Networks (2017)</b> [[Paper]](http://feedbacknet.stanford.edu/) [[Code]](https://github.com/amir32002/feedback-networks)
<p align="center"><img width="50%" src="https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/Feedback%20Networks.png" /></p>
:game_die: <b>Escape from Cells: Deep Kd-Networks for The Recognition of 3D Point Cloud Models (2017)</b> [[Paper]](http://www.arxiv.org/pdf/1704.01222.pdf)
@ -179,7 +179,7 @@ To see a survey of RGBD datasets, check out Michael Firman's [collection](http:/
<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/)
<b>Deep Sliding Shapes for Amodal 3D Object Detection in RGB-D Images (2016)</b> [[Paper]](http://dss.cs.princeton.edu/) [[Code]](https://github.com/shurans/DeepSlidingShape)
<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/)
@ -203,19 +203,19 @@ To see a survey of RGBD datasets, check out Michael Firman's [collection](http:/
<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="30%" src="http://people.scs.carleton.ca/~olivervankaick/cosegmentation/results6.png" /></p>
<b>Single-View Reconstruction via Joint Analysis of Image and Shape Collections (2015)</b> [[Paper]](https://www.cs.utexas.edu/~huangqx/modeling_sig15.pdf)
<b>Single-View Reconstruction via Joint Analysis of Image and Shape Collections (2015)</b> [[Paper]](https://www.cs.utexas.edu/~huangqx/modeling_sig15.pdf) [[Code]](https://github.com/huangqx/image_shape_align)
<p align="center"><img width="50%" src="http://vladlen.info/wp-content/uploads/2015/05/single-view.png" /></p>
<b>3D Shape Segmentation with Projective Convolutional Networks (2017)</b> [[Paper]](http://people.cs.umass.edu/~kalo/papers/shapepfcn/)
<b>3D Shape Segmentation with Projective Convolutional Networks (2017)</b> [[Paper]](http://people.cs.umass.edu/~kalo/papers/shapepfcn/) [[Code]](https://github.com/kalov/ShapePFCN)
<p align="center"><img width="50%" src="http://people.cs.umass.edu/~kalo/papers/shapepfcn/teaser.jpg" /></p>
<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/)
:game_die: <b>PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation (2017)</b> [[Paper]](http://stanford.edu/~rqi/pointnet/) [[Code]](https://github.com/charlesq34/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)
: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) [[Code]](https://github.com/charlesq34/pointnet2)
<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>
:game_die: <b>3D Graph Neural Networks for RGBD Semantic Segmentation (2017)</b> [[Paper]](http://www.cs.toronto.edu/~rjliao/papers/iccv_2017_3DGNN.pdf)
@ -242,7 +242,7 @@ Parsing of Large-scale 3D Point Clouds (2017)</b> [[Paper]](https://arxiv.org/pd
_Parametric Morphable Model-based methods_
<b>A Morphable Model For The Synthesis Of 3D Faces (1999)</b> [[Paper]](http://gravis.dmi.unibas.ch/publications/Sigg99/morphmod2.pdf)[[Github]](https://github.com/MichaelMure/3DMM)
<b>A Morphable Model For The Synthesis Of 3D Faces (1999)</b> [[Paper]](http://gravis.dmi.unibas.ch/publications/Sigg99/morphmod2.pdf)[[Code]](https://github.com/MichaelMure/3DMM)
<p align="center"><img width="40%" src="http://mblogthumb3.phinf.naver.net/MjAxNzAzMTdfMjcz/MDAxNDg5NzE3MzU0ODI3.9lQioLxwoGmtoIVXX9sbVOzhezoqgKMKiTovBnbUFN0g.sXN5tG4Kohgk7OJEtPnux-mv7OAoXVxxCyo3SGZMc6Yg.PNG.atelierjpro/031717_0222_DataDrivenS4.png?type=w420" /></p>
<b>The Space of Human Body Shapes: Reconstruction and Parameterization from Range Scans (2003)</b> [[Paper]](http://grail.cs.washington.edu/projects/digital-human/pub/allen03space-submit.pdf)
@ -319,10 +319,10 @@ _Deep Learning Methods_
:camera: <b>View Synthesis by Appearance Flow (2016)</b> [[Paper]](https://people.eecs.berkeley.edu/~tinghuiz/papers/eccv16_appflow.pdf) [[Code]](https://github.com/tinghuiz/appearance-flow)
<p align="center"><img width="50%" src="https://ai2-s2-public.s3.amazonaws.com/figures/2016-11-08/12280506dc8b5c3ca2db29fc3be694d9a8bef48c/6-Figure2-1.png" /></p>
:space_invader: <b>Voxlets: Structured Prediction of Unobserved Voxels From a Single Depth Image (2016)</b> [[Paper]](http://visual.cs.ucl.ac.uk/pubs/depthPrediction/http://visual.cs.ucl.ac.uk/pubs/depthPrediction/)
:space_invader: <b>Voxlets: Structured Prediction of Unobserved Voxels From a Single Depth Image (2016)</b> [[Paper]](http://visual.cs.ucl.ac.uk/pubs/depthPrediction/http://visual.cs.ucl.ac.uk/pubs/depthPrediction/) [[Code]](https://github.com/mdfirman/voxlets)
<p align="center"><img width="30%" src="https://i.ytimg.com/vi/1wy4y2GWD5o/maxresdefault.jpg" /></p>
:space_invader: <b>3D-R2N2: 3D Recurrent Reconstruction Neural Network (2016)</b> [[Paper]](http://cvgl.stanford.edu/3d-r2n2/)
:space_invader: <b>3D-R2N2: 3D Recurrent Reconstruction Neural Network (2016)</b> [[Paper]](http://cvgl.stanford.edu/3d-r2n2/) [[Code]](https://github.com/chrischoy/3D-R2N2)
<p align="center"><img width="50%" src="http://3d-r2n2.stanford.edu/imgs/overview.png" /></p>
:space_invader: <b>Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision (2016)</b> [[Paper]](https://eng.ucmerced.edu/people/jyang44/papers/nips16_ptn.pdf)
@ -349,16 +349,16 @@ _Deep Learning Methods_
:camera: <b>Synthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes with Deep Generative Networks (2017)</b> [[Paper]](http://openaccess.thecvf.com/content_cvpr_2017/papers/Soltani_Synthesizing_3D_Shapes_CVPR_2017_paper.pdf) [[Code]](https://github.com/Amir-Arsalan/Synthesize3DviaDepthOrSil)
<p align="center"><img width="50%" src="https://jiajunwu.com/images/spotlight_3dvae.jpg" /></p>
: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) [[Code]](https://github.com/lmb-freiburg/ogn)
<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>
:space_invader: <b>OctNetFusion: Learning Depth Fusion from Data (2017)</b> [[Paper]](https://arxiv.org/pdf/1704.01047.pdf)
:space_invader: <b>OctNetFusion: Learning Depth Fusion from Data (2017)</b> [[Paper]](https://arxiv.org/pdf/1704.01047.pdf) [[Code]](https://github.com/griegler/octnetfusion)
<p align="center"><img width="50%" src="https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/OctNetFusion-%20Learning%20Depth%20Fusion%20from%20Data.jpeg" /></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) [[Code]](https://github.com/fanhqme/PointSetGeneration)
<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>Learning Representations and Generative Models for 3D Point Clouds (2017)</b> [[Paper]](https://arxiv.org/pdf/1707.02392.pdf) [[Code]](https://github.com/optas/latent_3d_points)
@ -373,31 +373,31 @@ _Deep Learning Methods_
: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/) [[Code]](https://github.com/silverbottlep/tvsn)
<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/)
:camera: <b>3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks (2017)</b> [[Paper]](http://www.cs.unc.edu/~eunbyung/tvsn/) [[Code]](https://github.com/happylun/SketchModeling)
<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)
<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)
:camera::space_invader: <b>Weakly supervised 3D Reconstruction with Adversarial Constraint (2017)</b> [[Paper]](https://arxiv.org/pdf/1705.10904.pdf) [[Code]](https://github.com/jgwak/McRecon)
<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)
: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)
<p align="center"><img width="50%" src="http://kevinkaixu.net/projects/grass/teaser.jpg" /></p>
:gem: <b>Mesh-based Autoencoders for Localized Deformation Component Analysis (2017)</b> [[Paper]](https://arxiv.org/pdf/1709.04304.pdf)
<p align="center"><img width="50%" src="http://qytan.com/img/point_conv.jpg" /></p>
:gem: <b>Neural 3D Mesh Renderer (2017)</b> [[Paper]](http://hiroharu-kato.com/projects_en/neural_renderer.html)
:gem: <b>Neural 3D Mesh Renderer (2017)</b> [[Paper]](http://hiroharu-kato.com/projects_en/neural_renderer.html) [[Code]](https://github.com/hiroharu-kato/neural_renderer.git)
<p align="center"><img width="50%" src="https://pbs.twimg.com/media/DPSm-4HWkAApEZd.jpg" /></p>
:game_die::space_invader: <b>Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55 (2017)</b> [[Paper]](https://arxiv.org/pdf/1710.06104.pdf)
@ -415,10 +415,10 @@ _Deep Learning Methods_
<b>Analogy-Driven 3D Style Transfer (2014)</b> [[Paper]](http://www.chongyangma.com/publications/st/index.html)
<p align="center"><img width="50%" src="http://www.cs.ubc.ca/~chyma/publications/st/2014_st_teaser.png" /></p>
<b>Elements of Style: Learning Perceptual Shape Style Similarity (2015)</b> [[Paper]](http://people.cs.umass.edu/~zlun/papers/StyleSimilarity/StyleSimilarity.pdf)
<b>Elements of Style: Learning Perceptual Shape Style Similarity (2015)</b> [[Paper]](http://people.cs.umass.edu/~zlun/papers/StyleSimilarity/StyleSimilarity.pdf) [[Code]](https://github.com/happylun/StyleSimilarity)
<p align="center"><img width="50%" src="https://people.cs.umass.edu/~zlun/papers/StyleSimilarity/StyleSimilarity_teaser.jpg" /></p>
<b>Functionality Preserving Shape Style Transfer (2016)</b> [[Paper]](http://people.cs.umass.edu/~zlun/papers/StyleTransfer/StyleTransfer.pdf)
<b>Functionality Preserving Shape Style Transfer (2016)</b> [[Paper]](http://people.cs.umass.edu/~zlun/papers/StyleTransfer/StyleTransfer.pdf) [[Code]](https://github.com/happylun/StyleTransfer)
<p align="center"><img width="50%" src="https://people.cs.umass.edu/~zlun/papers/StyleTransfer/StyleTransfer_teaser.jpg" /></p>
<b>Unsupervised Texture Transfer from Images to Model Collections (2016)</b> [[Paper]](http://ai.stanford.edu/~haosu/papers/siga16_texture_transfer_small.pdf)
@ -427,7 +427,7 @@ _Deep Learning Methods_
<b>Learning Detail Transfer based on Geometric Features (2017)</b> [[Paper]](http://surfacedetails.cs.princeton.edu/)
<p align="center"><img width="50%" src="http://surfacedetails.cs.princeton.edu/images/teaser.png" /></p>
<b>Neural 3D Mesh Renderer (2017)</b> [[Paper]](http://hiroharu-kato.com/projects_en/neural_renderer.html)
<b>Neural 3D Mesh Renderer (2017)</b> [[Paper]](http://hiroharu-kato.com/projects_en/neural_renderer.html) [[Code]](https://github.com/hiroharu-kato/neural_renderer.git)
<p align="center"><img width="50%" src="https://pbs.twimg.com/media/DPSm-4HWkAApEZd.jpg" /></p>
<a name="scene_synthesis" />
@ -448,7 +448,7 @@ _Deep Learning Methods_
<b>IM2CAD</b> [[Paper]](http://homes.cs.washington.edu/~izadinia/im2cad.html)
<p align="center"><img width="50%" src="http://i.imgur.com/KhtOeuB.jpg" /></p>
<b>Raster-to-Vector: Revisiting Floorplan Transformation (2017, ICCV)</b> [[Paper]](http://www.cse.wustl.edu/~chenliu/floorplan-transformation.html)
<b>Raster-to-Vector: Revisiting Floorplan Transformation (2017, ICCV)</b> [[Paper]](http://www.cse.wustl.edu/~chenliu/floorplan-transformation.html) [[Code]](https://github.com/art-programmer/FloorplanTransformation)
<p align="center"><img width="50%" src="https://www.cse.wustl.edu/~chenliu/floorplan-transformation/teaser.png" /></p>
<a name="scene_understanding" />
@ -475,16 +475,16 @@ _Deep Learning Methods_
<b>DeLay: Robust Spatial Layout Estimation for Cluttered Indoor Scenes (2016)</b> [[Paper]](http://deeplayout.stanford.edu/)
<p align="center"><img width="30%" src="https://github.com/timzhang642/3D-Machine-Learning/blob/master/imgs/DeLay-Robust%20Spatial%20Layout%20Estimation%20for%20Cluttered%20Indoor%20Scenes.png" /></p>
<b>3D Semantic Parsing of Large-Scale Indoor Spaces (2016)</b> [[Paper]](http://buildingparser.stanford.edu/method.html)
<b>3D Semantic Parsing of Large-Scale Indoor Spaces (2016)</b> [[Paper]](http://buildingparser.stanford.edu/method.html) [[Code]](https://github.com/alexsax/2D-3D-Semantics)
<p align="center"><img width="50%" src="http://buildingparser.stanford.edu/images/teaser.png" /></p>
<b>Single Image 3D Interpreter Network (2016)</b> [[Paper]](http://3dinterpreter.csail.mit.edu/)
<b>Single Image 3D Interpreter Network (2016)</b> [[Paper]](http://3dinterpreter.csail.mit.edu/) [[Code]](https://github.com/jiajunwu/3dinn)
<p align="center"><img width="50%" src="http://3dinterpreter.csail.mit.edu/images/spotlight_3dinn_large.jpg" /></p>
<b>Deep Multi-Modal Image Correspondence Learning (2016)</b> [[Paper]](http://www.cse.wustl.edu/~chenliu/floorplan-matching.html)
<p align="center"><img width="50%" src="https://www.cse.wustl.edu/~furukawa/carousel/2016-floorplan-wide.png" /></p>
<b>Physically-Based Rendering for Indoor Scene Understanding Using Convolutional Neural Networks (2017)</b> [[Paper]](http://3dvision.princeton.edu/projects/2016/PBRS/)
<b>Physically-Based Rendering for Indoor Scene Understanding Using Convolutional Neural Networks (2017)</b> [[Paper]](http://3dvision.princeton.edu/projects/2016/PBRS/) [[Code]](https://github.com/yindaz/pbrs) [[Code]](https://github.com/yindaz/surface_normal) [[Code]](https://github.com/fyu/dilation) [[Code]](https://github.com/s9xie/hed)
<p align="center"><img width="50%" src="http://robots.princeton.edu/projects/2016/PBRS/teaser.jpg" /></p>
<b>RoomNet: End-to-End Room Layout Estimation (2017)</b> [[Paper]](https://arxiv.org/pdf/1703.06241.pdf)
@ -493,5 +493,5 @@ _Deep Learning Methods_
<b>SUN RGB-D: A RGB-D Scene Understanding Benchmark Suite (2017)</b> [[Paper]](http://rgbd.cs.princeton.edu/)
<p align="center"><img width="50%" src="http://rgbd.cs.princeton.edu/teaser.jpg" /></p>
<b>Semantic Scene Completion from a Single Depth Image (2017)</b> [[Paper]](http://sscnet.cs.princeton.edu/)
<b>Semantic Scene Completion from a Single Depth Image (2017)</b> [[Paper]](http://sscnet.cs.princeton.edu/) [[Code]](https://github.com/shurans/sscnet)
<p align="center"><img width="50%" src="http://sscnet.cs.princeton.edu/teaser.jpg" /></p>