  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/.
