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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.01844 |
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| _version_ | 1866908933414715392 |
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| author | Pieta, Pawel Tomasz Pedersen, Rasmus Juul Borgi, Sina Jørgensen, Jakob Sauer Andreasen, Jens Wenzel Dahl, Vedrana Andersen |
| author_facet | Pieta, Pawel Tomasz Pedersen, Rasmus Juul Borgi, Sina Jørgensen, Jakob Sauer Andreasen, Jens Wenzel Dahl, Vedrana Andersen |
| contents | Gaussian Splatting (GS) has emerged as a dominating technique for image rendering and has quickly been adapted for the X-ray Computed Tomography (CT) reconstruction task. However, despite being on par or better than many of its predecessors, the benefits of GS are typically not substantial enough to motivate a transition from well-established reconstruction algorithms. This paper addresses the most significant remaining limitations of the GS-based approach by introducing FaCT-GS, a framework for fast and flexible CT reconstruction. Enabled by an in-depth optimization of the voxelization and rasterization pipelines, our new method is significantly faster than its predecessors and scales well with projection and output volume size. Furthermore, the improved voxelization enables rapid fitting of Gaussians to pre-existing volumes, which can serve as a prior for warm-starting the reconstruction, or simply as an alternative, compressed representation. FaCT-GS is over 4X faster than the State of the Art GS CT reconstruction on standard 512x512 projections, and over 13X faster on 2k projections. Implementation available at: https://github.com/PaPieta/fact-gs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_01844 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FaCT-GS: Fast and Scalable CT Reconstruction with Gaussian Splatting Pieta, Pawel Tomasz Pedersen, Rasmus Juul Borgi, Sina Jørgensen, Jakob Sauer Andreasen, Jens Wenzel Dahl, Vedrana Andersen Computer Vision and Pattern Recognition Gaussian Splatting (GS) has emerged as a dominating technique for image rendering and has quickly been adapted for the X-ray Computed Tomography (CT) reconstruction task. However, despite being on par or better than many of its predecessors, the benefits of GS are typically not substantial enough to motivate a transition from well-established reconstruction algorithms. This paper addresses the most significant remaining limitations of the GS-based approach by introducing FaCT-GS, a framework for fast and flexible CT reconstruction. Enabled by an in-depth optimization of the voxelization and rasterization pipelines, our new method is significantly faster than its predecessors and scales well with projection and output volume size. Furthermore, the improved voxelization enables rapid fitting of Gaussians to pre-existing volumes, which can serve as a prior for warm-starting the reconstruction, or simply as an alternative, compressed representation. FaCT-GS is over 4X faster than the State of the Art GS CT reconstruction on standard 512x512 projections, and over 13X faster on 2k projections. Implementation available at: https://github.com/PaPieta/fact-gs. |
| title | FaCT-GS: Fast and Scalable CT Reconstruction with Gaussian Splatting |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.01844 |