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

arXiv: https://arxiv.org/abs/2406.02518 · HTML: https://mlsanity.com/paper/2406.02518/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 45 references that are in the map.
- First posted on arXiv 4 Jun 2024.

## Builds on (most cited first, 15 of 15)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [RadSplat: Radiance Field-Informed Gaussian Splatting for Robust Real-Time Rendering with 900+ FPS](https://mlsanity.com/paper/2403.13806/) (2024)
- [NAF: Neural Attenuation Fields for Sparse-View CBCT Reconstruction](https://mlsanity.com/paper/2209.14540/) (2022)
- [Radiative Gaussian Splatting for Efficient X-ray Novel View Synthesis](https://mlsanity.com/paper/2403.04116/) (2024)
- [DeepDRR -- A Catalyst for Machine Learning in Fluoroscopy-guided Procedures](https://mlsanity.com/paper/1803.08606/) (2018)
- [Deep Learning to Segment Pelvic Bones: Large-scale CT Datasets and Baseline Models](https://mlsanity.com/paper/2012.08721/) (2020)
- [Fast Auto-Differentiable Digitally Reconstructed Radiographs for Solving Inverse Problems in Intraoperative Imaging](https://mlsanity.com/paper/2208.12737/) (2022)
- [Relaxing Accurate Initialization Constraint for 3D Gaussian Splatting](https://mlsanity.com/paper/2403.09413/) (2024)
- [Volume Rendering Digest (for NeRF)](https://mlsanity.com/paper/2209.02417/) (2022)
- [Automatic Annotation of Hip Anatomy in Fluoroscopy for Robust and Efficient 2D/3D Registration](https://mlsanity.com/paper/1911.07042/) (2019)
- [Intraoperative 2D/3D Image Registration via Differentiable X-ray Rendering](https://mlsanity.com/paper/2312.06358/) (2023)
- [N-Dimensional Gaussians for Fitting of High Dimensional Functions](https://mlsanity.com/paper/2405.20067/) (2024)
- [iComMa: Inverting 3D Gaussian Splatting for Camera Pose Estimation via Comparing and Matching](https://mlsanity.com/paper/2312.09031/) (2023)
- [GaSpCT: Gaussian Splatting for Novel CT Projection View Synthesis](https://mlsanity.com/paper/2404.03126/) (2024)

## Built on by (most cited first, 23 of 23)

- [7DGS: Unified Spatial-Temporal-Angular Gaussian Splatting](https://mlsanity.com/paper/2503.07946/) (2025)
- [6DGS: Enhanced Direction-Aware Gaussian Splatting for Volumetric Rendering](https://mlsanity.com/paper/2410.04974/) (2024)
- [Volumetrically Consistent 3D Gaussian Rasterization](https://mlsanity.com/paper/2412.03378/) (2024)
- [X-GRM: Large Gaussian Reconstruction Model for Sparse-view X-rays to Computed Tomography](https://mlsanity.com/paper/2505.15235/) (2025)
- [4DRGS: 4D Radiative Gaussian Splatting for Efficient 3D Vessel Reconstruction from Sparse-View Dynamic DSA Images](https://mlsanity.com/paper/2412.12919/) (2024)
- [Render-FM: Feedforward Model for Real-time Photorealistic Volumetric Rendering](https://mlsanity.com/paper/2505.17338/) (2025)
- [InnerGS: Internal Scenes Reconstruction and Segmentation via Factorized 3D Gaussian Splatting](https://mlsanity.com/paper/2508.13287/) (2025)
- [NeRF-based CBCT Reconstruction needs Normalization and Initialization](https://mlsanity.com/paper/2506.19742/) (2025)
- [Active View Selection with Perturbed Gaussian Ensemble for Tomographic Reconstruction](https://mlsanity.com/paper/2603.06852/) (2026)
- [GriDiT: Factorized Grid-Based Diffusion for Efficient Long Image Sequence Generation](https://mlsanity.com/paper/2512.21276/) (2025)
- [GaussianPile: A Unified Sparse Gaussian Splatting Framework for Slice-based Volumetric Reconstruction](https://mlsanity.com/paper/2603.20611/) (2026)
- [XClipGS: Exact Half-Space Clipping for Medical Volume Gaussian Splatting](https://mlsanity.com/paper/2608.07760/) (2026)
- [LINGO: Latent Initialization and Gradient Optimization for Sparse-view X-ray Novel View Synthesis and CT Reconstruction with 3D Gaussian Splatting](https://mlsanity.com/paper/2609.22849/) (2026)
- [DaRePlane: Direction-aware Representations for Dynamic Scene Reconstruction](https://mlsanity.com/paper/2410.14169/) (2024)
- [IXGS-Intraoperative 3D Reconstruction from Sparse, Arbitrarily Posed Real X-rays](https://mlsanity.com/paper/2504.14699/) (2025)
- [RadGS-Reg: Registering Spine CT with Biplanar X-rays via Joint 3D Radiative Gaussians Reconstruction and 3D/3D Registration](https://mlsanity.com/paper/2508.21154/) (2025)
- [LamiGauss: Pitching Radiative Gaussian for Sparse-View X-ray Laminography Reconstruction](https://mlsanity.com/paper/2509.13863/) (2025)
- [UltraGS: Real-Time Physically-Decoupled Gaussian Splatting for Ultrasound Novel View Synthesis](https://mlsanity.com/paper/2511.07743/) (2025)
- [TG-Field: Geometry-Aware Radiative Gaussian Fields for Tomographic Reconstruction](https://mlsanity.com/paper/2602.11705/) (2026)
- [Projection-Volume Fidelity Divergence: Diagnosing and Controlling Optimization Drift in Sparse-View 3D Gaussian Tomography](https://mlsanity.com/paper/2606.22525/) (2026)
- [K-space Gaussian Representation for Parallel MRI](https://mlsanity.com/paper/2608.00075/) (2026)
- [Anatomy-Decomposed Chest Computed Tomography (CT) Projections as Scalable Supervision for Bone Suppression in Chest Radiographs](https://mlsanity.com/paper/2609.24937/) (2026)
- [Where Should the Gaussians Go? Reusable Label-Aware Allocation for Budget-Constrained Gaussian Splatting of Cinematic CT](https://mlsanity.com/paper/2610.20397/) (2026)

## Abstract (arXiv)

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.