用3D高斯点重建物体内部结构，支持文本控制分割。
InnerGS: Internal Scenes Reconstruction and Segmentation via Factorized 3D Gaussian Splatting
- 用内部分布的3D高斯建模连续体密度，直接重构内部结构
- 从稀疏切片数据重建出平滑精细的内部形态，无需相机位姿
- 支持自然语言查询实现医学图像文本引导分割，即插即用
3D高斯点阵（3DGS）近年来因其高效的场景渲染能力而受到关注，通过显式表示各向异性3D高斯点集来构建场景。然而，现有工作主要集中于外部表面建模。本文致力于内部场景重建，这对需要深入理解物体内部结构的应用至关重要。通过在内部3D高斯分布中直接建模连续体密度，我们的模型能够从稀疏切片数据中有效重建出平滑且细节丰富的内部结构。除了高保真重建外，我们进一步展示了该框架在下游任务中的潜力，如分割。通过集成语言特征，我们扩展了方法以实现基于自然语言查询的医学场景文本引导分割。本方法无需相机位姿，可即插即用，并天然兼容任意数据模态。代码已开源：https://github.com/Shuxin-Liang/InnerGS。
原文摘要 · Abstract (English)
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.
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