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DDGS-CT: Direction-Disentangled Gaussian Splatting for Realistic Volume Rendering

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arxiv 2406.02518 v2 pith:F3X5MW7E submitted 2024-06-04 cs.CV eess.IV

classification cs.CVeess.IV
keywords accuracyx-rayanalyticalanisotropicapplicationsddgsdirection-disentangledefficiency
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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 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. RadGS-Reg: Registering Spine CT with Biplanar X-rays via Joint 3D Radiative Gaussians Reconstruction and 3D/3D Registration

    cs.CV 2025-08 conditional novelty 6.0 of 10

    A joint 3D Gaussian-splatting-based reconstruction and registration network registers spine CT to two X-rays with 1.14 mm mean error in 0.82 seconds on a small in-house set.

  2. 4DRGS: 4D Radiative Gaussian Splatting for Efficient 3D Vessel Reconstruction from Sparse-View Dynamic DSA Images

    eess.IV 2024-12 conditional novelty 6.0 of 10

    4DRGS models vessels as 4D radiative Gaussians with a neural attenuation field and reconstructs 3D vessel volumes from sparse-view dynamic DSA images in about 5 to 13 minutes, matching or beating prior state-of-the-ar...

  3. Volumetrically Consistent 3D Gaussian Rasterization

    cs.CV 2024-12 conditional novelty 6.0 of 10

    A drop-in alpha computation for 3DGS rasterizers, derived from analytic volumetric transmittance, improves edge and perceptual metrics and transfers to tomography.

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