GSoC: Incorporating point-based convolutions in TensorFlow Graphics (7/12)

requirements.txt

@@ -13,3 +13,5 @@ termcolor >= 1.1.0
 trimesh >= 2.37.22
 # Required by trimesh.
 networkx
+# required for pytests of project/point_convolutions
+sklearn

tensorflow_graphics/projects/point_convolutions/.flake8

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+          .pytest_cache,
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+          tensorflow_graphics/submodules/*
+ignore = C901,
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tensorflow_graphics/projects/point_convolutions/LICENSE

@@ -0,0 +1,202 @@
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tensorflow_graphics/projects/point_convolutions/README.md

@@ -0,0 +1,70 @@
+# TensorFlow Graphics Point Cloud Convolutions
+
+This module contains a python TensorFlow module `pylib` and a custom ops package in `tfg_custom_ops`.
+While it is possible to run without the custom ops package, it is strongly advised to install it for performance and memory efficiency.
+
+## Content
+
+This code contains all necessary operations to perform point cloud convolutions
+
+1. Datastructure
+    - Point cloud class for batches of arbitrary sized point clouds.
+    - Memory efficient regular grid data structure
+2. Point cloud operations
+    - Neighborhood computation
+    - Point density estimation
+    - Spatial sampling
+      - Poisson Disk sampling
+      - Cell average sampling
+3. Convolution kernels 
+    - Kernel Point Convolutions
+      - linear interpolation
+      - gaussian interpolation
+      - deformable points with regularization loss as in [KPConv](https://arxiv.org/abs/1904.08889)
+    - MLP
+      - multiple MLPs as in [MCConv](https://arxiv.org/abs/1806.01759)
+      - single MLP as in [PointConv](https://arxiv.org/abs/1811.07246)
+4. Feature aggregation inside receptive fields
+    - Monte-Carlo integration with pdf
+    - Constant summation
+5. Easy to use classes for building models
+    - `PointCloud` class
+    - `PointHierarchy` for sequential downsampling of point clouds
+    - layer classes
+      - `MCConv`
+      - `PointConv`
+      - `KPConv`
+      - `Conv1x1`
+
+## Installation
+
+Precompiled versions of the custom ops package are provided in `custom_ops/pkg_builds/tf_*` for the latest TensorFlow versions.
+For compilation instructions see the [README](custom_ops/README.md) in the `custom_ops` folder.
+
+To install it run the following command (replace `VERSION` with your installed TensorFlow version, e.g. `2.3.0`)
+```bash
+  pip install custom_ops/tf_VERSION/*.whl
+```
+
+## Tutorials
+
+Check out the Colab notebooks for an introduction to the code
+
+- [Introduction](pylib/notebooks/Introduction.ipynb)
+- [Classification on ModelNet40](pylib/notebooks/ModelNet40.ipynb) 
+
+## Unit tests
+
+Unit tests can be evaluated using
+
+```bash
+  pip install -r pytest_requirements.txt
+  pytest pylib/
+```
+
+These include tests of the custom ops if they are installed.
+
+## Additional Information
+
+You may use this software under the
+[Apache 2.0 License](https://github.com/schellmi42/tensorflow_graphics_point_clouds/blob/master/LICENSE).

tensorflow_graphics/projects/point_convolutions/pylib/__init__.py

@@ -0,0 +1,15 @@
+# Copyright 2020 The TensorFlow Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific
+
+from pylib import pc
+# from pylib import io