From bf754ef3f3813e3566cbeab484cf282fb6e9f7af Mon Sep 17 00:00:00 2001 From: "Yuxuan (Tim) Zhang" Date: Sun, 10 Dec 2017 15:43:37 -0500 Subject: [PATCH] Update README.md --- README.md | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 919de91..7afe82f 100644 --- a/README.md +++ b/README.md @@ -27,7 +27,7 @@ To make it a collaborative project, you may add content throught pull requests o ## 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) @@ -113,6 +113,9 @@ To see a survey of RGBD datasets, I recommend to check out Michael Firman's [col VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection (2017) [[Paper]](https://arxiv.org/pdf/1711.06396.pdf)

+Frustum PointNets for 3D Object Detection from RGB-D Data (2017) [[Paper]](https://arxiv.org/pdf/1711.08488.pdf) +

+ ## Part Segmentation @@ -303,6 +306,9 @@ _Deep Learning Methods_ :gem: Mesh-based Autoencoders for Localized Deformation Component Analysis (2017) [[Paper]](https://arxiv.org/pdf/1709.04304.pdf)

+:gem: Neural 3D Mesh Renderer (2017) [[Paper]](http://hiroharu-kato.com/projects_en/neural_renderer.html) +

+ ## Style Transfer @@ -327,6 +333,9 @@ _Deep Learning Methods_ Learning Detail Transfer based on Geometric Features (2017) [[Paper]](http://surfacedetails.cs.princeton.edu/)

+Neural 3D Mesh Renderer (2017) [[Paper]](http://hiroharu-kato.com/projects_en/neural_renderer.html) +

+ ## Scene Synthesis