ACM Transactions on Graphics (SIGGRAPH'26)

Gaussian Point Splatting

Joris Rijsdijk, Christoph Peters, Michael Weinmann, and Ricardo Marroquim

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We propose Gaussian point splatting, a stochastic method to render Gaussian splats that scales extremely well to scenes with many Gaussians. Our core idea is to sample pixel-sized, opaque points from the Gaussians and to splat them to a framebuffer using 64-bit atomics. Through parallel programming primitives, we achieve an even distribution of the workload across millions of threads. Since these threads splat points independently, multiple points may splat to the same pixel. That makes it non-trivial to determine how many points should be splatted for a Gaussian or how they should be distributed to achieve the desired opacity. We successfully formalize and solve these problems, thus keeping our renders faithful to the original Gaussian splatting. To further accelerate our method, we employ hierarchical frustum and occlusion culling. Our method renders hundreds of millions of Gaussians in real time. The only differences compared to the original Gaussian splatting are slight noise and differences in aliasing.

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@article{bib:rijsdijk:2026,
    author       = { Rijsdijk, Joris and Peters, Christoph and Weinmann, Michael and Marroquim, Ricardo },    
    title        = { Gaussian Point Splatting },
    journal      = { ACM Transactions on Graphics (SIGGRAPH'26) },
    volume       = { 45 },
    number       = { 4 },
    year         = { 2026 },
    publisher    = { Association for Computing Machinery },
    doi          = { 10.1145/3811272 },
    url          = { https://publications.graphics.tudelft.nl/papers/854 },
}