This package is a PyTorch library that provides functionality for differentiable rasterization.

It consists of five main components:

• transform
• rasterize
• render
• interpolate
• edge_grad

There are also optional components such as msi and mipmap_grid_sampler. New components may be added in the future.

A typical flow looks like this:

transform → rasterize → render → interpolate → CUSTOM SHADING → edge_grad

where:

• transform: projects the vertex positions from camera space to image space
• rasterize: performs rasterization, where pixels in the output image are associated with triangles
• render: computes depth and baricentric image
• interpolate: interpolates arbitrary vertex attributes
• CUSTOM SHADING: user implemented shading
• edge_grad: special module that computes gradients for the rasterize step, which is not differentiable on its own. For details, please see Rasterized Edge Gradients: Handling Discontinuities Differentiably

The "Hello Triangle" with DRTK would look like this:

import drtk
import torch as th
from torchvision.utils import save_image  # to save images

# create vertex buffer of shape [1 x n_vertices x 3], here for triangle `n_vertices` == 3
v = th.as_tensor([[[0, 511, 1], [255, 0, 1], [511, 511, 1]]]).float().cuda()

# create index buffer
vi = th.as_tensor([[0, 1, 2]]).int().cuda()

# rasterize
index_img = drtk.rasterize(v, vi, height=512, width=512)

# compute baricentrics
_, bary = drtk.render(v, vi, index_img)

# we won't do shading, we'll just save the baricentrics and filter out the empty region
# which is marked with `-1` in `index_img`
img = bary * (index_img != -1)

save_image(img, "render.png")

• PyTorch >= 2.1.0

To build a wheel and install it:

pip install git+https://github.com/facebookresearch/DRTK.git

To build inplace, which is useful for package development:

python setup.py build_ext --inplace -j 1

See the CONTRIBUTING file for how to help out.

DRTK is MIT licensed, as found in the LICENSE file.

@mish{pidhorskyi2024rasterized,
  title        = {Rasterized Edge Gradients: Handling Discontinuities Differentiably},
  author       = {Pidhorskyi, Stanislav and Simon, Tomas and Schwartz, Gabriel and Wen, He and Sheikh, Yaser and Saragih, Jason},
  howpublished = {arXiv preprint},
  year         = {2024},
  note         = {arXiv:2405.02508}
}