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Differential Gaussian Rasterization

What's new : Except for the RGB image, we also support render depth map, alpha map, normal map and extra per-Gaussian attributes (both forward and backward process) compared with the original repository.

:mega: We recently support computing the gradient w.r.t. camera pose. However, this feature hasn't been fully validated, thus we don't merge it into the main branch. You can switch to pose branch for more information. If you find any bugs, leave a message in the issues. Thank you!

We modify the dependency name as diff_gauss to avoid dependecy conflict with the original version. You can install our repo by executing the following command lines

git clone --recurse-submodules https://github.com/slothfulxtx/diff-gaussian-rasterization.git 
cd diff-gaussian-rasterization
python setup.py install

Here's an example of our modified differential gaussian rasterization repo

from diff_gauss import GaussianRasterizationSettings, GaussianRasterizer

rendered_image, rendered_depth, rendered_norm, rendered_alpha, radii, extra = rasterizer(
    means3D = means3D,
    means2D = means2D,
    shs = shs,
    colors_precomp = colors_precomp,
    opacities = opacity,
    scales = scales,
    rotations = rotations,
    cov3Ds_precomp = cov3D_precomp,
    extra_attrs = extra_attrs
)

Details:

Used as the rasterization engine for the paper "3D Gaussian Splatting for Real-Time Rendering of Radiance Fields". If you can make use of it in your own research, please be so kind to cite us.

<section class="section" id="BibTeX"> <div class="container is-max-desktop content"> <h2 class="title">BibTeX</h2> <pre><code>@Article{kerbl3Dgaussians, author = {Kerbl, Bernhard and Kopanas, Georgios and Leimk{\"u}hler, Thomas and Drettakis, George}, title = {3D Gaussian Splatting for Real-Time Radiance Field Rendering}, journal = {ACM Transactions on Graphics}, number = {4}, volume = {42}, month = {July}, year = {2023}, url = {https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/} }</code></pre> </div> </section>