# InnerGS: Internal Scenes Reconstruction and Segmentation via Factorized 3D Gaussian Splatting

arXiv: https://arxiv.org/abs/2508.13287 · HTML: https://mlsanity.com/paper/2508.13287/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 27 references that are in the map.
- First posted on arXiv 18 Aug 2025.

## Builds on (most cited first, 22 of 22)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [4D Gaussian Splatting for Real-Time Dynamic Scene Rendering](https://mlsanity.com/paper/2310.08528/) (2023)
- [DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation](https://mlsanity.com/paper/2309.16653/) (2023)
- [pixelSplat: 3D Gaussian Splats from Image Pairs for Scalable Generalizable 3D Reconstruction](https://mlsanity.com/paper/2312.12337/) (2023)
- [LangSplat: 3D Language Gaussian Splatting](https://mlsanity.com/paper/2312.16084/) (2023)
- [Splatter Image: Ultra-Fast Single-View 3D Reconstruction](https://mlsanity.com/paper/2312.13150/) (2023)
- [COLMAP-Free 3D Gaussian Splatting](https://mlsanity.com/paper/2312.07504/) (2023)
- [Structure-Aware Sparse-View X-ray 3D Reconstruction](https://mlsanity.com/paper/2311.10959/) (2023)
- [R2-Gaussian: Rectifying Radiative Gaussian Splatting for Tomographic Reconstruction](https://mlsanity.com/paper/2405.20693/) (2024)
- [MedNeRF: Medical Neural Radiance Fields for Reconstructing 3D-aware CT-Projections from a Single X-ray](https://mlsanity.com/paper/2202.01020/) (2022)
- [Large-Vocabulary Segmentation for Medical Images with Text Prompts](https://mlsanity.com/paper/2312.17183/) (2023)
- [Radiative Gaussian Splatting for Efficient X-ray Novel View Synthesis](https://mlsanity.com/paper/2403.04116/) (2024)
- [Gamba: Marry Gaussian Splatting with Mamba for single view 3D reconstruction](https://mlsanity.com/paper/2403.18795/) (2024)
- [3D Gaussian Splatting in Robotics: A Survey](https://mlsanity.com/paper/2410.12262/) (2024)
- [Deceptive-NeRF/3DGS: Diffusion-Generated Pseudo-Observations for High-Quality Sparse-View Reconstruction](https://mlsanity.com/paper/2305.15171/) (2023)
- [DDGS-CT: Direction-Disentangled Gaussian Splatting for Realistic Volume Rendering](https://mlsanity.com/paper/2406.02518/) (2024)
- [Gaussian Pancakes: Geometrically-Regularized 3D Gaussian Splatting for Realistic Endoscopic Reconstruction](https://mlsanity.com/paper/2404.06128/) (2024)
- [Advancing Extended Reality with 3D Gaussian Splatting: Innovations and Prospects](https://mlsanity.com/paper/2412.06257/) (2024)
- [Neural Radiance Fields in Medical Imaging: A Survey](https://mlsanity.com/paper/2402.17797/) (2024)
- [Multi-Layer Gaussian Splatting for Immersive Anatomy Visualization](https://mlsanity.com/paper/2410.16978/) (2024)
- [X-GRM: Large Gaussian Reconstruction Model for Sparse-view X-rays to Computed Tomography](https://mlsanity.com/paper/2505.15235/) (2025)
- [FruitNinja: 3D Object Interior Texture Generation with Gaussian Splatting](https://mlsanity.com/paper/2411.12089/) (2024)

## Built on by (most cited first, 3 of 3)

- [GaussianPile: A Unified Sparse Gaussian Splatting Framework for Slice-based Volumetric Reconstruction](https://mlsanity.com/paper/2603.20611/) (2026)
- [Medical Scene Reconstruction and Segmentation based on 3D Gaussian Representation](https://mlsanity.com/paper/2512.22800/) (2025)
- [K-space Gaussian Representation for Parallel MRI](https://mlsanity.com/paper/2608.00075/) (2026)

## Abstract (arXiv)

3D Gaussian Splatting (3DGS) has recently gained popularity for efficient scene rendering by representing scenes as explicit sets of anisotropic 3D Gaussians. However, most existing work focuses primarily on modeling external surfaces. In this work, we target the reconstruction of internal scenes, which is crucial for applications that require a deep understanding of an object's interior. By directly modeling a continuous volumetric density through the inner 3D Gaussian distribution, our model effectively reconstructs smooth and detailed internal structures from sparse sliced data. Beyond high-fidelity reconstruction, we further demonstrate the framework's potential for downstream tasks such as segmentation. By integrating language features, we extend our approach to enable text-guided segmentation of medical scenes via natural language queries. Our approach eliminates the need for camera poses, is plug-and-play, and is inherently compatible with any data modalities. We provide cuda implementation at: https://github.com/Shuxin-Liang/InnerGS.