mlsanityData to 14 Oct 2026

X-GRM: Large Gaussian Reconstruction Model for Sparse-view X-rays to Computed Tomography

arXiv 2505.15235 ↗First posted 21 May 2025eess.IV cs.CVcited by 6

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Abstract (arXiv)

Computed Tomography serves as an indispensable tool in clinical workflows, providing non-invasive visualization of internal anatomical structures. Existing CT reconstruction works are limited to small-capacity model architecture and inflexible volume representation. In this work, we present X-GRM (X-ray Gaussian Reconstruction Model), a large feedforward model for reconstructing 3D CT volumes from sparse-view 2D X-ray projections. X-GRM employs a scalable transformer-based architecture to encode sparse-view X-ray inputs, where tokens from different views are integrated efficiently. Then, these tokens are decoded into a novel volume representation, named Voxel-based Gaussian Splatting (VoxGS), which enables efficient CT volume extraction and differentiable X-ray rendering. This combination of a high-capacity model and flexible volume representation, empowers our model to produce high-quality reconstructions from various testing inputs, including in-domain and out-domain X-ray projections. Our codes are available at: https://github.com/CUHK-AIM-Group/X-GRM.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 55 of its 77 references that are in the map. First posted on arXiv 21 May 2025.

Builds on

55 of 77 references.

References · 55

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38 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 3.

6 papers.