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## 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/).
## Contents
## Table of Contents
- [Single Object Classification](#single_classification)
- [Multiple Objects Detection](#multiple_detection)
- [Part Segmentation](#part_segmentation)
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<b>VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection (2017)</b> [[Paper]](https://arxiv.org/pdf/1711.06396.pdf)
<p align="center"><img width="50%" src="https://pbs.twimg.com/media/DPMtLhHXUAcQUj2.jpg" /></p>
<b>Frustum PointNets for 3D Object Detection from RGB-D Data (2017)</b> [[Paper]](https://arxiv.org/pdf/1711.08488.pdf)
<p align="center"><img width="50%" src="https://arxiv-sanity-sanity-production.s3.amazonaws.com/render-output/17548/fig/teaser.svg" /></p>
<a name="part_segmentation" />
## Part Segmentation
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: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)
<p align="center"><img width="50%" src="https://pbs.twimg.com/media/DPSm-4HWkAApEZd.jpg" /></p>
<a name="style_transfer" />
## Style Transfer
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<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)
<p align="center"><img width="50%" src="https://pbs.twimg.com/media/DPSm-4HWkAApEZd.jpg" /></p>
<a name="scene_synthesis" />
## Scene Synthesis