Reliable three-dimensional (3D) reconstruction from endoscopic video is essential for endoscopic digital twins, scene review, and minimally invasive visual analysis. However, endoscopic images are not clean observations of intrinsic tissue appearance. Depth-dependent blur, shallow mucosal color diffusion, wet-surface specular reflection, and frame-wise color variation are often coupled with the captured signal. When such observation-dependent effects are directly optimized as Gaussian colors, conventional 3D Gaussian Splatting may encode transient imaging artifacts as persistent tissue appearance, leading to blurred textures, color drift, specular residues, and unstable novel-view synthesis. This paper presents EndoDGS (Endoscopic Degradation-Decoupled Gaussian Splatting), a degradation-decoupled Gaussian Splatting framework for endoscopic novel-view reconstruction. The core idea is to keep stable geometry and base tissue appearance in the Gaussian representation, while modeling endoscope-induced degradations separately in a bounded render-space compensation pipeline. EndoDGS combines lightweight appearance modulation for frame-wise color stabilization with sequential degradation compensation for optical blur, mucosal color transport, and wet-surface specular response. This design reduces the entanglement between persistent tissue appearance and transient imaging degradations without changing the underlying Gaussian geometry and visibility ordering. Experiments on synthetic colonoscopy and real endoscopic/laparoscopic datasets covering 38 scenes show that EndoDGS consistently improves reconstruction quality over representative implicit and explicit reconstruction baselines. The results demonstrate that separating stable tissue representation from observation-dependent endoscopic degradations provides a more faithful, stable, and interpretable foundation for endoscopic 3D reconstruction.
Dong et al. (Tue,) studied this question.