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README.md

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-# test1
+# 3D Machine Learning
+In recent years, tremendous amount of progress is being made in the field of 3D Machine Learning, which is an interdisciplinary field that fuses computer vision, computer graphics and machine learning. This repo is derived from my study notes and will be used as a place for triaging new research papers. 
+
+I'll use the following to differentiate 3D representations:
+* :camera: Multi-view Images
+* :space_invader: Volumetric
+* :game_die: Point Cloud
+* :gem: Polygonal Mesh
+* :pill: Primitive-based
+
+## Get Involved
+To make it a collaborative project, you may add content throught pull requests or open an issue to let me know. 
+
+## Available Courses
+[Stanford CS468: Machine Learning for 3D Data (Spring 2017)](http://graphics.stanford.edu/courses/cs468-17-spring/)
+
+[MIT 6.838: Shape Analysis (Spring 2017)](http://groups.csail.mit.edu/gdpgroup/6838_spring_2017.html)
+
+[Princeton COS 526: Advanced Computer Graphics  (Fall 2010)](https://www.cs.princeton.edu/courses/archive/fall10/cos526/syllabus.php)
+
+[Princeton CS597: Geometric Modeling and Analysis (Fall 2003)](https://www.cs.princeton.edu/courses/archive/fall03/cs597D/)
+
+## Datasets
 aaa
+
+## Single Object Classification
+aaa
+
+## Multiple Objects Detection
+aaa
+
+## Part Segmentation
+aaa
+
+## 3D Synthesis/Reconstruction
+aaa
+
+## 3D Style Transfer
+aaa
+
+[Generative Adversarial Nets] [[Paper]](https://arxiv.org/abs/1406.2661)[[Code]](https://github.com/goodfeli/adversarial)
+
+[Generative Adversarial Nets] [[Paper]](https://arxiv.org/abs/1406.2661)
+
+[Generative Adversarial Nets] [[Paper]](https://arxiv.org/abs/1406.2661)
+
+## title 2
+
+
+<p align="center"><img width="20%" src="imgs/a.jpg" /></p>