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
this https URL

通过使用新型的基于神经场的方法 EndoSurf，本文针对 3D 场景的无效表示问题，从 RGBD 序列中学习有效地表示变形表面的形状和纹理，实现了重建立体内软组织的高保真度，且在公共内窥镜数据集上明显优于现有解决方案。