Initial license and readme

Author: phermosilla

LICENSE

@@ -0,0 +1,25 @@
+MCCNN: Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Clouds
+
+Copyright (c) 2018, Visual Computing group form Ulm University, Germany
+
+The MIT License (MIT)
+
+Copyright (c) 2018 Pedro Hermosilla
+
+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

@@ -0,0 +1,66 @@
+### MCCNN: *Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Clouds*
+Created by <a href="https://www.uni-ulm.de/en/in/mi/institute/mi-mitarbeiter/pedro-hermosilla-casajus/" target="_blank">Pedro Hermosilla</a>, <a href="http://www.homepages.ucl.ac.uk/~ucactri/">Tobias Ritschel</a>, <a href="https://www.cs.upc.edu/~ppau/index.html" target="_blank">Pere-Pau Vazquez</a>, <a href="https://www.cs.upc.edu/~alvar/" target="_blank">Alvar Vinacua</a>, <a href="https://www.uni-ulm.de/in/mi/institut/mi-mitarbeiter/tr/" target="_blank">Timo Ropinski</a>.
+
+![prediction example]()
+
+### Citation
+If you find this code useful please consider citing us:
+
+        @article{hermosilla2018mccnn,
+          title={Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Clouds},
+          author={Hermosilla, P. and Ritschel, T. and Vazquez, P-P and Vinacua, A. and Ropinski, T.},
+          journal={arXiv preprint arXiv:1806.01759},
+          year={2018}
+        }
+
+### Introduction
+
+We propose an efficient and effective method to learn convolutions for non-uniformly sampled point clouds, as they are obtained with modern acquisition techniques. Learning is enabled by four key novelties: first, representing the convolution kernel itself as a multilayer perceptron; second, phrasing convolution as a Monte Carlo integration problem, third, constructing an unstructured Poisson disk hierarchy for pooling, and fourth, using Monte Carlo convolution as pooling and upsampling operation at different resolutions simultaneously. The key idea across all these contributions is to guarantee adequate consideration of the underlying non-uniform sample distribution function from a Monte Carlo perspective. To make the proposed concepts applicable for real-world tasks, we propose an efficient implementation which significantly reduces the required GPU memory. By employing our method in hierarchical network architectures we can outperform most of the state-of-the-art networks on established point cloud segmentation, classification and normal estimation benchmarks. Furthermore, in contrast to most existing approaches, our method is robust to sampling variations even when only trained on uniformly sampled models.
+
+In this repository we release the code of our tensor operations and network architectures for classification, segmentation and normal estimation tasks, which realize the ideas presented in our <a href="https://arxiv.org/abs/1806.01759">arxiv paper</a>. For further detials of the techniques implmemented here you can refer to the papaer.
+
+### Installation
+
+First, install <a href="https://www.tensorflow.org/install/">TensorFlow</a>. The code presented here was developed using TensorFlow v1.5 GPU version, python 2.7, and Ubuntu 16.04 TLS. However, it should also work with TensorFlow v1.8 GPU version and python 3. All the operation where implemented on GPU, no CPU implementation is provided. Therefore, a workstation with a state-of-the-art GPU is required.
+
+#### Compiling tensorflow operations
+
+In order to train the networks provided in this repository, first we have to compile the new tensor operations which implement the Monte Carlo convolutions. These operations are located on the folder `tf_ops`. To compile them we should execute the following commands:
+
+    python genCompileScript.py --cudaFolder *path_to_cuda*
+    sh compile.sh
+
+
+### Tasks
+
+
+
+#### Classification
+
+
+MCClassSmall:   python ModelNet.py --grow 128 --useDropOut
+                python ModelNetEval.py --grow 128
+
+MCClass:        python ModelNet.py --model MCClass --useDropOut --useDropOutConv
+                python ModelNetEval.py --model MCClass
+
+MCClassH:       python ModelNet.py --model MCClassH --useDropOut --useDropOutConv
+                python ModelNetEval.py --model MCClassH
+
+#### Segmentation
+
+
+MCSeg:          python ShapeNet.py --useDropOut
+                python ShapeNetEval.py
+
+#### Normal Estimation
+
+
+MCNorm:         python ModelNetNormals.py
+                python ModelNetNormalsEval.py
+
+MCNormSmall:    python ModelNetNormals.py --model MCNormSmall
+                python ModelNetNormalsEval.py --model MCNormSmall
+
+#### Custom Architectures
+