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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.00219 |
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| _version_ | 1866913079735877632 |
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| author | Chen, Jingxiang Ibrahim, Mohamed Liu, Yang |
| author_facet | Chen, Jingxiang Ibrahim, Mohamed Liu, Yang |
| contents | 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} |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_00219 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | VkSplat: High-Performance 3DGS Training in Vulkan Compute Chen, Jingxiang Ibrahim, Mohamed Liu, Yang Computer Vision and Pattern Recognition 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} |
| title | VkSplat: High-Performance 3DGS Training in Vulkan Compute |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.00219 |