first commit

.gitignore

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+.gamesh/*
+log/*
+out/*
+*.pyc
+models/*.pyc
+utils/*.pyc

LICENSE

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+MIT License
+
+Copyright (c) 2020 Nitin Agarwal
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+# GAMesh: Guided and Augmented Meshing for Deep Point Networks
+### 3DV 2020 [[Paper]](https://arxiv.org/abs/2010.09774)[[Project Page]](https://www.ics.uci.edu/~agarwal/GAMesh/index.html)
+
+We propose a new meshing algorithm to generate a surface with correct topology for the output points of a point network. GAMesh can be used both in post-processing to mesh the output points or to train the point network to directly optimize the vertex positions of the final 3D mesh. Unlike traditional surface reconstruction techniques (like Ball-Pivot Algorithm & Screened Poisson Reconstruction), GAMesh is independent of both the density and distribution of points and gaurantees correct geometry and topology of the final surface. We show its applications in single-view shape prediction, fair evaluation of point networks and reconstructing surfaces for networks which output sparse point clouds. 
+
+## Getting Started
+coming soon
+
+## Citation
+If you find this code useful, please consider citing our paper
+
+```
+@inproceedings{agarwal2020GAMesh,
+    title={GAMesh: Guided and Augmented Meshing for Deep Point Networks},
+    author={Agarwal, Nitin and Gopi, M},
+    booktitle={3DV},
+    year={2020}}
+```
+
+## License
+Our code is released under MIT license (see License file for details)
+
+## Contact
+Please contact [Nitin Agarwal](https://www.ics.uci.edu/~agarwal/) if you have questions or comments