VkSplat: High-Performance 3DGS Training in Vulkan Compute

arXiv cs.CV / 5/4/2026

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Key Points

  • The paper introduces VkSplat, a 3D Gaussian Splatting (3DGS) training pipeline implemented entirely using Vulkan compute for cross-vendor compatibility.
  • The authors report major performance gains of 3.3× and a 33% reduction in VRAM usage versus a CUDA+PyTorch baseline while preserving output quality.
  • The approach includes multiple optimizations aimed at overcoming performance and compatibility limits seen in existing 3DGS training pipelines.
  • The work claims to be the first fully Vulkan-based 3DGS training pipeline that reaches state-of-the-art performance, and provides code via GitHub.

Abstract

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\times 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: \href{https://github.com/harry7557558/vksplat}{https://github.com/harry7557558/vksplat}