Endo-4DGX: Robust Endoscopic Scene Reconstruction and Illumination Correction with Gaussian Splatting
Journal:
arXiv
Published Date:
Jun 29, 2025
Abstract
Accurate reconstruction of soft tissue is crucial for advancing automation in
image-guided robotic surgery. The recent 3D Gaussian Splatting (3DGS)
techniques and their variants, 4DGS, achieve high-quality renderings of dynamic
surgical scenes in real-time. However, 3D-GS-based methods still struggle in
scenarios with varying illumination, such as low light and over-exposure.
Training 3D-GS in such extreme light conditions leads to severe optimization
problems and devastating rendering quality. To address these challenges, we
present Endo-4DGX, a novel reconstruction method with illumination-adaptive
Gaussian Splatting designed specifically for endoscopic scenes with uneven
lighting. By incorporating illumination embeddings, our method effectively
models view-dependent brightness variations. We introduce a region-aware
enhancement module to model the sub-area lightness at the Gaussian level and a
spatial-aware adjustment module to learn the view-consistent brightness
adjustment. With the illumination adaptive design, Endo-4DGX achieves superior
rendering performance under both low-light and over-exposure conditions while
maintaining geometric accuracy. Additionally, we employ an exposure control
loss to restore the appearance from adverse exposure to the normal level for
illumination-adaptive optimization. Experimental results demonstrate that
Endo-4DGX significantly outperforms combinations of state-of-the-art
reconstruction and restoration methods in challenging lighting environments,
underscoring its potential to advance robot-assisted surgical applications. Our
code is available at https://github.com/lastbasket/Endo-4DGX.
