Learning View-Dependent Splatting Kernels

Huakeng Ding^* · Zhangpeng Liu^* · Fan Pei · Kun Zhou · Hongzhi Wu

^*contributed equally

SIGGRAPH 2026

Project Page | Paper | arXiv | Code

We present a novel differentiable framework to automatically learn view-dependent 2D kernels in a splatting-based pipeline, that are tailored to efficiently represent various types of scenes.

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Branches

This repository organizes code by experiment type using separate branches. Each branch is independent and can be cloned directly:

git clone -b <branch-name> https://github.com/optkernel/codebase

Branch	Description
2d_splatting	2D splatting experiments
3d_splatting	3D splatting experiments
2d_image_representation	2D image representation experiments

Please refer to the README in each branch for environment setup and usage instructions.

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Citation

Cite as below if you find this repository is helpful to your project:

@inproceedings{ding2026kernel,
    title     = {Learning View-Dependent Splatting Kernels},
    author    = {Huakeng Ding and Zhangpeng Liu and Fan Pei and Kun Zhou and Hongzhi Wu},
    booktitle = {SIGGRAPH 2026 Conference Papers},
    year      = {2026}
}

Acknowledgments

We have intensively borrowed code from 3D gaussian splatting, 2D Gaussian Splatting, Beta-Splatting and 3DGS-MCMC. Many thanks to the authors for sharing their codes.