Differentiable Volumetric Rendering

@inproceedings{DVR,
    title = {Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision},
    author = {Niemeyer, Michael and Mescheder, Lars and Oechsle, Michael and Geiger, Andreas},
    booktitle = {Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
    year = {2020}
}

conda env create -f environment.yaml
conda activate dvr

python setup.py build_ext --inplace

You can now test our code on the provided input images in the `demo` folder. To this end, start the generation process for one of the config files in the `configs/demo` folder. For example, simply run ``` python generate.py configs/demo/demo_combined.yaml ``` This script should create a folder `out/demo/demo_combined` where the output meshes are stored. The script will copy the inputs into the `generation/inputs` folder and creates the meshes in the `generation/meshes` folder. Moreover, the script creates a `generation/vis` folder where both inputs and outputs are copied together. ## Dataset ### Download Datasets To evaluate a pre-trained model or train a new model from scratch, you have to obtain the respective dataset. We use three different datasets in the DVR project:

1. ShapeNet for 2.5D supervised models (using the Choy et. al. renderings as input and our renderings as supervision)
2. ShapeNet for 2D supervised models (using the Kato et. al. renderings)
3. A subset of the DTU multi-view dataset

You can download our preprocessed data using ``` bash scripts/download_data.sh ``` and following the instructions. The sizes of the datasets are 114GB (a), 34GB (b), and 0.5GB (c). This script should download and unpack the data automatically into the `data` folder. ### Data Convention Please have a look at the [FAQ](https://github.com/autonomousvision/differentiable_volumetric_rendering/blob/master/FAQ.md) for details regarding the type of camera matrices we use. ## Usage When you have installed all binary dependencies and obtained the preprocessed data, you are ready to run our pre-trained models and train new models from scratch. ### Generation To generate meshes using a trained model, use ``` python generate.py CONFIG.yaml ``` where you replace `CONFIG.yaml` with the correct config file. The easiest way is to use a pre-trained model. You can do this by using one of the config files which are indicated with `_pretrained.yaml`. For example, for our 2.5D supervised single-view reconstruction model run ``` python generate.py configs/single_view_reconstruction/multi_view_supervision/ours_depth_pretrained.yaml ``` or for our multi-view reconstruction from RGB images and sparse depth maps for the birds object run ``` python generate.py configs/multi_view_reconstruction/birds/ours_depth_mvs_pretrained.yaml ``` Our script will automatically download the model checkpoints and run the generation. You can find the outputs in the `out/.../pretrained` folders. Please note that the config files `*_pretrained.yaml` are only for generation, not for training new models: when these configs are used for training, the model will be trained from scratch, but during inference our code will still use the pre-trained model. ### Generation From Your Own Single Images Similar to our demo, you can easily generate 3D meshes from your own single images. To this end, create a folder which contains your own images (e.g. `media/my_images`). Next, you can reuse the config file `configs/demo/demo_combined.yaml` and just adjust the [data - path](https://github.com/autonomousvision/differentiable_volumetric_rendering/blob/4ba785a2bfee0cc28324fbbd96c6e5e83f6899fc/configs/demo/demo_combined.yaml#L4) and [training - out_dir](https://github.com/autonomousvision/differentiable_volumetric_rendering/blob/4ba785a2bfee0cc28324fbbd96c6e5e83f6899fc/configs/demo/demo_combined.yaml#L6) arguments to your needs. For example, you can set the config file to ``` inherit_from: configs/single_view_reconstruction/multi_view_supervision/ours_combined_pretrained.yaml data: dataset_name: images path: media/my_images training: out_dir: out/my_3d_models ``` to generate 3D models for the images in `media/my_images`. The models will be saved to `out/my_3d_models`. Similar to before, to start the generation process, run ``` python generate.py configs/demo/demo_combined.yaml ``` *Note:* You can only expect our model to provide reasonable results on data which is similar to what it was trained on (white background, single object, etc.). ### Evaluation For evaluation of the models, we provide the script `eval_meshes.py`. You can run it using ``` python eval_meshes.py CONFIG.yaml ``` The script takes the meshes generated in the previous step and evaluates them using a standardized protocol. The output will be written to `.pkl`/`.csv` files in the corresponding generation folder which can be processed using [pandas](https://pandas.pydata.org/). *Note:* We follow previous works to use "use 1/10 times the maximal edge length of the current object’s bounding box as unit 1" (see [Section 4 - Metrics](http://www.cvlibs.net/publications/Mescheder2019CVPR.pdf)). In practise, that means that we multiply the Chamfer-L1 metric by a factor of 10 for reporting the numbers in the paper. ### Training Finally, to train a new network from scratch, run ``` python train.py CONFIG.yaml ``` where you replace `CONFIG.yaml` with the name of the configuration file you want to use. You can monitor on the training process using [tensorboard](https://www.tensorflow.org/guide/summaries_and_tensorboard): ``` cd OUTPUT_DIR tensorboard --logdir ./logs ``` where you replace `OUTPUT_DIR` with the respective output directory. For available training options, please take a look at `configs/default.yaml`. # Futher Information ## More Work on Implicit Representations If you like the DVR project, please check out other works on implicit representions from our group: - [Mescheder et. al. - Occupancy Networks: Learning 3D Reconstruction in Function Space (CVPR 2019)](https://avg.is.tuebingen.mpg.de/publications/occupancy-networks) - [Oechsle et. al. - Texture Fields: Learning Texture Representations in Function Space (ICCV 2019)](https://avg.is.tuebingen.mpg.de/publications/oechsle2019iccv) - [Niemeyer et. al. - Occupancy Flow: 4D Reconstruction by Learning Particle Dynamics (ICCV 2019)](https://avg.is.tuebingen.mpg.de/publications/niemeyer2019iccv) - [Peng et. al. - Convolutional Occupancy Networks (ArXiv 2020)](https://arxiv.org/abs/2003.04618) - [Oechsle et. al. - Learning Implicit Surface Light Fields (ArXiv 2020)](https://arxiv.org/abs/2003.12406) ## Other Relevant Works Also check out other exciting works on inferring implicit representations without 3D supervision: - [Liu et. al. - Learning to Infer Implicit Surfaces without 3D Supervision (NeurIPS 2019)](https://arxiv.org/abs/1911.00767) - [Sitzmann et. al. - Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations (NeurIPS 2019)](https://arxiv.org/abs/1906.01618) - [Liu. et. al. - DIST: Rendering Deep Implicit Signed Distance Function with Differentiable Sphere Tracing (CVPR 2020)](http://b1ueber2y.me/projects/DIST-Renderer) - [Mildenhall et. al. - NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis (ArXiv 2020)](https://arxiv.org/abs/2003.08934)