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Reconstructing soft tissues from stereo endoscope videos is an essentialprerequisite for many medical applications. Previous methods struggle toproduce high-quality geometry and appearance due to their inadequaterepresentations of 3D scenes. To address this issue, we propose a novelneural-field-based method, called EndoSurf, which effectively learns torepresent a deforming surface from an RGBD sequence. In EndoSurf, we modelsurface dynamics, shape, and texture with three neural fields. First, 3D pointsare transformed from the observed space to the canonical space using thedeformation field. The signed distance function (SDF) field and radiance fieldthen predict their SDFs and colors, respectively, with which RGBD images can besynthesized via differentiable volume rendering. We constrain the learned shapeby tailoring multiple regularization strategies and disentangling geometry andappearance. Experiments on public endoscope datasets demonstrate that EndoSurfsignificantly outperforms existing solutions, particularly in reconstructinghigh-fidelity shapes. Code is available athttps://github.com/Ruyi-Zha/endosurf.git.

EndoSurf: Neural Surface Reconstruction of Deformable Tissues with Stereo Endoscope Videos