DDGS-CT: Direction-Disentangled Gaussian Splatting for Realistic Volume Rendering
Zhongpai Gao, Benjamin Planche, Meng Zheng, Xiao Chen, Terrence Chen, Ziyan Wu
Abstract
Digitally reconstructed radiographs (DRRs) are simulated 2D X-ray images generated from 3D CT volumes, widely used in preoperative settings but limited in intraoperative applications due to computational bottlenecks, especially for accurate but heavy physics-based Monte Carlo methods. While analytical DRR renderers offer greater efficiency, they overlook anisotropic X-ray image formation phenomena, such as Compton scattering. We present a novel approach that marries realistic physics-inspired X-ray simulation with efficient, differentiable DRR generation using 3D Gaussian splatting (3DGS). Our direction-disentangled 3DGS (DDGS) method separates the radiosity contribution into isotropic and direction-dependent components, approximating complex anisotropic interactions without intricate runtime simulations. Additionally, we adapt the 3DGS initialization to account for tomography data properties, enhancing accuracy and efficiency. Our method outperforms state-of-the-art techniques in image accuracy. Furthermore, our DDGS shows promise for intraoperative applications and inverse problems such as pose registration, delivering superior registration accuracy and runtime performance compared to analytical DRR methods.
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Cited by top-tier papers7
- R2-Gaussian: Rectifying Radiative Gaussian Splatting for Tomographic ReconstructionRuyi Zha, Tao Jun Lin, Yuanhao Cai, Jiwen Cao et al.NeurIPS 2024 · 99 citations
- 7DGS: Unified Spatial-Temporal-Angular Gaussian SplattingZhongpai Gao, Benjamin Planche, Meng Zheng, Anwesa Choudhuri et al.ICCV 2025 · 3 citations
- TG-Field: Geometry-Aware Radiative Gaussian Fields for Tomographic ReconstructionYuxiang Zhong, Jun Wei, Chaoqi Chen, Senyou An et al.AAAI 2026
- 6DGS: Enhanced Direction-Aware Gaussian Splatting for Volumetric RenderingZhongpai Gao, Benjamin Planche, Meng Zheng, Anwesa Choudhuri et al.ICLR 2025
- GaussianPile: A Unified Sparse Gaussian Splatting Framework for Slice-based Volumetric ReconstructionDi Kong, Yikai Wang, Wenjie Guo, Yifan Bu et al.CVPR 2026
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- 3D Gaussian Splatting for Real-Time Radiance Field RenderingBernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, George DrettakisSIGGRAPH 2023 · 5,687 citations
- Plenoxels: Radiance Fields without Neural NetworksSara Fridovich-Keil, Alex Yu, Matthew Tancik, Qinhong Chen et al.CVPR 2022 · 1,237 citations
- A non-exponential transmittance model for volumetric scene representationsDelio Vicini, Wenzel Jakob, Anton KaplanyanSIGGRAPH 2021 · 25 citations
- N-Dimensional Gaussians for Fitting of High Dimensional FunctionsStavros Diolatzis, Tobias Zirr, Alexander Kuznetsov, Georgios Kopanas et al.SIGGRAPH 2024 · 8 citations
- Intraoperative 2D/3D Image Registration via Differentiable X-Ray RenderingVivek Gopalakrishnan, Neel Dey, Polina GollandCVPR 2024
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