Release code

.gitignore

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+*.pyc
+/build
+/diffshadow.egg-info

README.md

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-# 
-
 <p align="center">
 
   <h1 align="center"><a href="https://mworchel.github.io/differentiable-shadow-mapping">Differentiable Shadow Mapping for Efficient Inverse Graphics</a></h1>
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     ·
     <a href="https://www.cg.tu-berlin.de/team/prof-dr-marc-alexa/"><strong>Marc Alexa</strong></a>
   </p>
-
-  <h3 align="center">coming soon</h3>
 </p>
+
+## About
+
+This repository contains the official implementation of the paper "Differentiable Shadow Mapping for Efficient Inverse Graphics", which presents a technique for efficient shadow rendering in the context of differentiable rasterization.
+The implementation is based on the differentiable rasterization primitives from [nvdiffrast](https://github.com/NVlabs/nvdiffrast).
+
+Similar to nvdiffrast, we provide a set of flexible low-level primitives for differentiable shadow mapping. These primitives are meant to be used when implementing a custom differentiable renderer with shadow mapping. They are contained in the module `diffshadow.shadow`: 
+- `render_shadow_map`: Render the scene(s) from the point-of-view of the light(s) and generate a shadow map with the necessary information (e.g. the depth for standard shadow mapping)
+- `filter_shadow_map`: Apply the anti-aliasing operation of nvdiffrast and convolve the shadow map(s) with a filter kernel
+- `compute_visibility`: From the (filtered) shadow map(s) compute the visibility for an array of points in the scene
+
+In addition, we provide a simple renderer that implements a high-level interface to our shadow mapping primitives. A variant of this renderer has been used to generate the results in the paper. The package `diffshadow.simple_renderer` contains the renderer and utilities for scene generation and geometry processing.
+
+## Getting Started 
+
+Setup the environment and install basic requirements using conda
+
+```bash
+conda env create -f environment.yml
+conda activate diffshadow
+```
+
+In the directory where you have cloned this repository to, install our package by running
+
+```bash
+pip install .
+```
+
+Now, you can run any of the notebooks provided in this repository:
+- `1_getting_started.ipynb`: Introduction to the simple renderer and its scene representation with a basic shadow-driven pose optimization
+- `2_shadow_art.ipynb`: Simple shadow art experiments
+- `3_minimal_plane.ipynb`: Reproduction of Figure 5 in the paper (necessity for applying anti-aliasing to the shadow map)
+- `4_low_level_primitives.ipynb`: Examples for how to use the low-level shadow mapping primitives
+
+
+
+Alternatively, if you want to use the implementation without running any notebooks, you might install our package by simply running
+
+```bash
+pip install git+https://github.com/mworchel/differentiable-shadow-mapping
+```
+
+## Common Issues
+
+[TODO]
+
+## Citation
+
+If you find this code or our method useful for your academic research, please cite our paper
+
+```bibtex
+@InProceedings{worchel:2023:diff_shadow_mapping,
+      author    = {Worchel, Markus and Alexa, Marc},
+      title     = {Differentiable Shadow Mapping for Efficient Inverse Graphics},
+      booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+      month     = {June},
+      year      = {2023},
+      pages     = {142-153}
+  }
+```