EndoSparse: Real-Time Sparse View Synthesis of Endoscopic Scenes using Gaussian Splatting
This addresses the challenge of sparse-view reconstruction in endoscopic surgery, enabling more practical deployment of neural 3D reconstruction in clinical scenarios, though it appears incremental as it builds on existing neural rendering techniques.
The paper tackles the problem of 3D reconstruction from sparse endoscopic images, which is common in clinical settings, and proposes EndoSparse, a framework that uses prior knowledge from foundation models to improve geometric and appearance quality, achieving superior results with as few as three views.
3D reconstruction of biological tissues from a collection of endoscopic images is a key to unlock various important downstream surgical applications with 3D capabilities. Existing methods employ various advanced neural rendering techniques for photorealistic view synthesis, but they often struggle to recover accurate 3D representations when only sparse observations are available, which is usually the case in real-world clinical scenarios. To tackle this {sparsity} challenge, we propose a framework leveraging the prior knowledge from multiple foundation models during the reconstruction process, dubbed as \textit{EndoSparse}. Experimental results indicate that our proposed strategy significantly improves the geometric and appearance quality under challenging sparse-view conditions, including using only three views. In rigorous benchmarking experiments against state-of-the-art methods, \textit{EndoSparse} achieves superior results in terms of accurate geometry, realistic appearance, and rendering efficiency, confirming the robustness to sparse-view limitations in endoscopic reconstruction. \textit{EndoSparse} signifies a steady step towards the practical deployment of neural 3D reconstruction in real-world clinical scenarios. Project page: https://endo-sparse.github.io/.