update README and add license file

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2020 Zixin Luo
+
+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

@@ -169,3 +169,21 @@ Download the data (validation/test) [Link](https://vision.uvic.ca/imw-challenge/
 ```bash
 cd /local/aslfeat && python evaluations.py --config configs/imw2020_eval.yaml
 ```
+
+## Misc 
+
+1. Training data is provided in [GL3D](https://github.com/lzx551402/GL3D).
+
+2. You might be also interested in a 3D local feature, [D3Feat](https://github.com/XuyangBai/D3Feat/).
+
+# Acknowledgements
+
+1. The backbone networks and the learning scheme is heavily borrowed from [D2-Net](https://github.com/mihaidusmanu/d2-net).
+
+2. We thank you the authors of [R2D2](https://github.com/naver/r2d2) for sharing their evaluation results on HPatches that helped us plot Fig.1. The updated results of R2D2 are even more excited.
+
+3. We refer to the public implementation of [SuperPoint](https://github.com/rpautrat/SuperPoint) for organizing the code and implementing the evaluation metrics.
+
+4. We implement the modulated DCN referring to [this](https://github.com/DHZS/tf-deformable-conv-layer/blob/master/nets/deformable_conv_layer.py). The current implementation is not efficient, and we expect a native implementation in TensorFlow to be available in the future.
+
+5. We thank for [Sida Peng](https://pengsida.net/) for sharing his experience in reproducing this work, also pointing out the flaws in our implementation of evaluation metrics.