# WebSplatter: Enabling Cross-Device Efficient Gaussian Splatting in Web Browsers via WebGPU

arXiv: https://arxiv.org/abs/2602.03207 · HTML: https://mlsanity.com/paper/2602.03207/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 43 references that are in the map.
- First posted on arXiv 3 Feb 2026.

## Builds on (most cited first, 8 of 8)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Compact 3D Gaussian Representation for Radiance Field](https://mlsanity.com/paper/2311.13681/) (2023)
- [DiffSplat: Repurposing Image Diffusion Models for Scalable Gaussian Splat Generation](https://mlsanity.com/paper/2501.16764/) (2025)
- [DISTWAR: Fast Differentiable Rendering on Raster-based Rendering Pipelines](https://mlsanity.com/paper/2401.05345/) (2023)
- [AdR-Gaussian: Accelerating Gaussian Splatting with Adaptive Radius](https://mlsanity.com/paper/2409.08669/) (2024)
- [A Memory Bandwidth-Efficient Hybrid Radix Sort on GPUs](https://mlsanity.com/paper/1611.01137/) (2016)
- [WebGPU-SPY: Finding Fingerprints in the Sandbox through GPU Cache Attacks](https://mlsanity.com/paper/2401.04349/) (2024)

## Abstract (arXiv)

We present WebSplatter, an end-to-end GPU rendering pipeline for the heterogeneous web ecosystem. Unlike naive ports, WebSplatter introduces a wait-free hierarchical radix sort that circumvents the lack of global atomics in WebGPU, ensuring deterministic execution across diverse hardware. Furthermore, we propose an opacity-aware geometry culling stage that dynamically prunes splats before rasterization, significantly reducing overdraw and peak memory footprint. Evaluation demonstrates that WebSplatter consistently achieves 1.2× to 4.5× speedups over state-of-the-art web viewers.