Dynamic Gaussian Splatting With Efficient Depth Optimization and Regularization for Sparse-View Endoscopic Scene Reconstruction

Abstract

In endoscopic surgery, reconstructing dynamic scenes using 4D Gaussian Splatting (4DGS) offers high-quality results but suffers from severe geometric instability under sparse-view conditions. This instability stems from geometrically inconsistent priors and the lack of a synergistic regularization scheme. To address these root causes, we introduce a novel framework built upon two complementary modules. First, a Depth Optimization framework produces a scale-consistent geometric prior by fusing multi-resolution depth maps and anchoring the scale via sparse structure-from-motion (SfM) points. Second, a Multi-faceted Geometric Regularization scheme employs a trio of complementary constraints (across global-local depth, virtual views, and surface normals) that work in synergy to mitigate severe overfitting. Extensive experiments on two public surgical datasets demonstrate superior sparse-view reconstruction performance over representative baselines at real-time rendering speeds. This work delivers a robust and efficient solution for high-fidelity 4D scene reconstruction in minimally invasive endoscopy.