InnerGS: Internal Scenes Rendering via Factorized 3D Gaussian Splatting
This addresses the need for deep understanding of object interiors in applications like medical imaging or industrial inspection, representing a novel method for a known bottleneck.
The paper tackles the problem of reconstructing internal scenes from sparse sliced data, achieving smooth and detailed internal structures without requiring camera poses.
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. 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.