Reconstructing soft tissues from stereo endoscope videos is an essential prerequisite for many medical applications. Previous methods struggle to produce high-quality geometry and appearance due to their inadequate representations of 3D scenes. To address this issue, we propose a novel neural-field-based method, called EndoSurf, which effectively learns to represent a deforming surface from an RGBD sequence. In EndoSurf, we model surface dynamics, shape, and texture with three neural fields. First, 3D points are transformed from the observed space to the canonical space using the deformation field. The signed distance function (SDF) field and radiance field then predict their SDFs and colors, respectively, with which RGBD images can be synthesized via differentiable volume rendering. We constrain the learned shape by tailoring multiple regularization strategies and disentangling geometry and appearance. Experiments on public endoscope datasets demonstrate that EndoSurf significantly outperforms existing solutions, particularly in reconstructing high-fidelity shapes. Code is available at https://github.com/Ruyi-Zha/endosurf.git.

翻译：从立体内窥镜视频中重建软组织是许多医学应用的基本前提。由于对三维场景的表示能力不足，以往的方法难以生成高质量的几何形状和外观。为解决这一问题，我们提出了一种名为EndoSurf的新型神经场方法，该方法能够有效地从RGBD序列中学习表示变形表面。在EndoSurf中，我们利用三个神经场分别对表面动力学、形状和纹理进行建模。首先，通过变形场将三维点从观测空间变换到规范空间。然后，符号距离函数（SDF）场和辐射场分别预测这些点的SDF值和颜色，进而通过可微分体渲染合成RGBD图像。我们通过定制多种正则化策略并解耦几何与外观来约束学习到的形状。在公共内窥镜数据集上的实验表明，EndoSurd在重建高保真形状方面显著优于现有解决方案。代码发布于https://github.com/Ruyi-Zha/endosurf.git。