# VkSplat: High-Performance 3DGS Training in Vulkan Compute

arXiv: https://arxiv.org/abs/2605.00219 · HTML: https://mlsanity.com/paper/2605.00219/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 13 references that are in the map.
- First posted on arXiv 30 Apr 2026.

## Builds on (most cited first, 11 of 11)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [gsplat: An Open-Source Library for Gaussian Splatting](https://mlsanity.com/paper/2409.06765/) (2024)
- [3D Gaussian Splatting as Markov Chain Monte Carlo](https://mlsanity.com/paper/2404.09591/) (2024)
- [Taming 3DGS: High-Quality Radiance Fields with Limited Resources](https://mlsanity.com/paper/2406.15643/) (2024)
- [StopThePop: Sorted Gaussian Splatting for View-Consistent Real-time Rendering](https://mlsanity.com/paper/2402.00525/) (2024)
- [Speedy-Splat: Fast 3D Gaussian Splatting with Sparse Pixels and Sparse Primitives](https://mlsanity.com/paper/2412.00578/) (2024)
- [FastGS: Training 3D Gaussian Splatting in 100 Seconds](https://mlsanity.com/paper/2511.04283/) (2025)
- [TC-GS: A Faster Gaussian Splatting Module Utilizing Tensor Cores](https://mlsanity.com/paper/2505.24796/) (2025)
- [Efficient Differentiable Hardware Rasterization for 3D Gaussian Splatting](https://mlsanity.com/paper/2505.18764/) (2025)
- [Moment-Based 3D Gaussian Splatting: Resolving Volumetric Occlusion with Order-Independent Transmittance](https://mlsanity.com/paper/2512.11800/) (2025)

## Built on by (most cited first, 1 of 1)

- [VkVIO: Cross-platform GPU Acceleration for Visual-Inertial Odometry with Vulkan](https://mlsanity.com/paper/2609.30459/) (2026)

## Abstract (arXiv)

We present VkSplat, a high-performance, cross-vendor 3D Gaussian Splatting (3DGS) training pipeline implemented fully in Vulkan compute, addressing performance and compatibility limitation of existing training pipelines. With various optimizations, we achieve 3.3× speed and 33% VRAM reduction over CUDA+PyTorch baseline, maintaining quality, and demonstrating compatibility across GPU vendors. To the best of our knowledge, this is the first fully-Vulkan-based 3DGS training pipeline that achieves state-of-the-art performance. Code: hrefhttps://github.com/harry7557558/vksplathttps://github.com/harry7557558/vksplat