Official implementation for our CVPR 2022 Paper "GenDR: A Generalized Differentiable Renderer".

Paper @ ArXiv, Video @ Youtube.

gendr can be installed via pip from PyPI with

pip install gendr

⚠️ Note that gendr requires CUDA, the CUDA Toolkit (for compilation), and torch>=1.9.0 (matching the CUDA version).

Alternatively, GenDR may be installed from source, e.g., in a virtual environment like

virtualenv -p python3 .env1
. .env1/bin/activate
pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html
pip install .

Make sure that the CUDA version of PyTorch (e.g., cu111 for CUDA 11.1) matches the locally installed version. However, on some machines, compiling works only with specific subversions that may be unequal to the local subversion, so a potential quick fix is trying different PyTorch version and CUDA subversion combinations.

A differentiable renderer may be defined as follows

import gendr

diff_renderer = gendr.GenDR(
    image_size=256,
    dist_func='uniform',
    dist_scale=0.01,
    dist_squared=False,
    aggr_alpha_func='probabilistic',
    aggr_rgb_func='hard',
)

In the following, we provide the entire set of arguments of GenDR. The most important parameters are marked in bold. For the essential parameters dist_func and aggr_alpha_func, we give a set of options. For a reference, see the paper.

• image_size the size of the rendered image (default: 256)

• background_color (default: [0, 0, 0])

• anti_aliasing render it at 2x the resolution and average to reduce aliasing (default: False)

• dist_func the distribution used for the differentiable occlusion test (default: uniform)

  • hard hard, non-differentiable rendering, Dirac delta distribution, Heaviside function (alias heaviside)
  • uniform uniform distribution
  • cubic_hermite Cubic-Hermite sigmoid function
  • wigner_semicircle Wigner Semicircle distribution
  • gaussian Gaussian Distribution
  • laplace Laplace Distribution
  • logistic logistic Distribution
  • gudermannian Gudermannian function, hyperbolic secant distribution (alias hyperbolic_secant)
  • cauchy Cauchy distribution
  • reciprocal reciprocal sigmoid function
  • gumbel_max Gumbel-max distribution
  • gumbel_min Gumbel-min distribution
  • exponential exponential distribution
  • exponential_rev exponential distribution (reversed / mirrored)
  • gamma gamma distribution
  • gamma_rev gamma distribution (reversed / mirrored)
  • levy Levy distribution
  • levy_rev Levy distribution (reversed / mirrored)

• dist_scale the scale parameter of the distribution, tau in the paper (default: 1e-2)

• dist_squared optionally, use the square-root distribution of dist_func (default: False)

• dist_shape for some distributions, we need a shape parameter (default: None)

• dist_shift for some distributions, we need an optional shift parameter (default: None or 0)

• dist_eps pixels further away than dist_scale*dist_eps are ignored for performance reasons (default: 1e4)

• aggr_alpha_func the t-conorm used to aggregate occlusion values (default: probabilistic)

  • hard to be used with dist_func='hard'
  • max maximum T-conorm
  • probabilistic probabilistic T-conorm
  • einstein Einstein sum T-conorm
  • hamacher Hamacher T-conorm
  • frank Frank T-conorm
  • yager Yager T-conorm
  • aczel_alsina Aczel-Alsina T-conorm
  • dombi Dombi T-conorm
  • schweizer_sklar Schweizer-Sklar T-conorm

• aggr_alpha_t_conorm_p for some t-conorms, we need a shape parameter (default: None)

• aggr_rgb_func (default: softmax)

• aggr_rgb_eps (default: 1e-3)

• aggr_rgb_gamma (default: 1e-3)

• near value for the viewing frustum (default: 1)

• far value for the viewing frustum (default: 100)

• double_side render all faces from both sides (default: False)

• texture_type type of texture sampling (default: surface; options: surface, vertex)

python experiments/opt_shape.py -sq --gif

python experiments/opt_camera.py -sq --gif

Optimal default parameters for --dist_scale are automatically used in the script for the set of distributions and t-conorms that are benchmarked on this task in the paper.

python experiments/train_reconstruction.py --distribution uniform --t_conorm probabilistic

@inproceedings{petersen2022gendr,
  title={{GenDR: A Generalized Differentiable Renderer}},
  author={Petersen, Felix and Goldluecke, Bastian and Borgelt, Christian and Deussen, Oliver},
  booktitle={IEEE/CVF International Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2022}
}

gendr is released under the MIT license. See LICENSE for additional details about it.