Endoscopic procedures are crucial for colorectal cancer diagnosis, and three-dimensional reconstruction of the environment for real-time novel-view synthesis can significantly enhance diagnosis. We present PR-ENDO, a framework that leverages 3D Gaussian Splatting within a physically based, relightable model tailored for the complex acquisition conditions in endoscopy, such as restricted camera rotations and strong view-dependent illumination. By exploiting the connection between the camera and light source, our approach introduces a relighting model to capture the intricate interactions between light and tissue using physically based rendering and MLP. Existing methods often produce artifacts and inconsistencies under these conditions, which PR-ENDO overcomes by incorporating a specialized diffuse MLP that utilizes light angles and normal vectors, achieving stable reconstructions even with limited training camera rotations. We benchmarked our framework using a publicly available dataset and a newly introduced dataset with wider camera rotations. Our methods demonstrated superior image quality compared to baseline approaches.
内窥镜手术在结直肠癌诊断中至关重要，而通过实时新视图合成进行环境的三维重建，可以显著提升诊断效果。我们提出了 PR-ENDO，一个框架结合了 3D Gaussian Splatting (3DGS) 和基于物理的可重光照模型，专为内窥镜复杂采集条件（如有限的相机旋转和强视角相关光照）而设计。 通过利用相机和光源之间的关联，PR-ENDO 引入了一种重光照模型，利用基于物理的渲染和多层感知机（MLP）捕获光与组织之间复杂的交互。在这些条件下，现有方法常常生成伪影和不一致的结果，而 PR-ENDO 通过加入一个专门设计的漫反射 MLP，结合光角度和法向量，有效实现了在有限训练相机旋转下的稳定重建。 我们使用一个公开数据集和一个包含更广相机旋转的新引入数据集对框架进行了基准测试。实验结果表明，PR-ENDO 在图像质量上相比基准方法表现出显著优势。