TG-Field: Geometry-Aware Radiative Gaussian Fields for Tomographic Reconstruction

• URL: http://arxiv.org/abs/2602.11705v1
• Date: Thu, 12 Feb 2026 08:33:01 GMT
• Title: TG-Field: Geometry-Aware Radiative Gaussian Fields for Tomographic Reconstruction
• Authors: Yuxiang Zhong, Jun Wei, Chaoqi Chen, Senyou An, Hui Huang,
• Abstract summary: Tomographic Geometry Field (TG-Field) is a geometry-aware Gaussian deformation framework for computed tomography (CT) reconstruction.<n> TG-Field consistently outperforms existing methods, achieving state-of-the-art reconstruction accuracy under highly sparse-view conditions.
• Score: 16.246538335191982
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D Gaussian Splatting (3DGS) has revolutionized 3D scene representation with superior efficiency and quality. While recent adaptations for computed tomography (CT) show promise, they struggle with severe artifacts under highly sparse-view projections and dynamic motions. To address these challenges, we propose Tomographic Geometry Field (TG-Field), a geometry-aware Gaussian deformation framework tailored for both static and dynamic CT reconstruction. A multi-resolution hash encoder is employed to capture local spatial priors, regularizing primitive parameters under ultra-sparse settings. We further extend the framework to dynamic reconstruction by introducing time-conditioned representations and a spatiotemporal attention block to adaptively aggregate features, thereby resolving spatiotemporal ambiguities and enforcing temporal coherence. In addition, a motion-flow network models fine-grained respiratory motion to track local anatomical deformations. Extensive experiments on synthetic and real-world datasets demonstrate that TG-Field consistently outperforms existing methods, achieving state-of-the-art reconstruction accuracy under highly sparse-view conditions.

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