CG-3DGS: Complexity-Guided 3D Gaussian Splatting for High-Fidelity Surgical Scene Reconstruction

Abstract

Accurate 3D reconstruction in surgical scenarios is essential for visualizing dynamic tissues with complex anatomical geometries. While 3D Gaussian Splatting (3D-GS) has been explored as an efficient approach to scene modeling, occlusion-induced voids and suboptimal detail optimization have limited its application in surgery. This work introduces a Complexity-Guided 3D Gaussian Splatting (CG-3DGS) framework, in which occlusion regions are globally filled by a state-of-the-art optical flow-based video inpainting method. A frequency–spatial aware refinement (FSAR) mechanism is proposed, allowing spectral signatures and spatial gradients to be jointly analyzed to enhance critical anatomical features (e.g., blood vessels). This mechanism adaptively guides Gaussian densification based on scene-specific anatomical complexity. Experimental results demonstrate that the proposed framework achieves higher reconstruction fidelity while maintaining efficient rendering speeds.