DynoSurf: Neural Deformation-based Temporally Consistent Dynamic Surface
  Reconstruction
Yuxin Yao Siyu Ren Junhui Hou Zhi Deng Juyong Zhang Wenping Wang
This paper explores the problem of reconstructing temporally consistent
surfaces from a 3D point cloud sequence without correspondence. To address this
challenging task, we propose DynoSurf, an unsupervised learning framework
integrating a template surface representation with a learnable deformation
field. Specifically, we design a coarse-to-fine strategy for learning the
template surface based on the deformable tetrahedron representation.
Furthermore, we propose a learnable deformation representation based on the
learnable control points and blending weights, which can deform the template
surface non-rigidly while maintaining the consistency of the local shape.
Experimental results demonstrate the significant superiority of DynoSurf over
current state-of-the-art approaches, showcasing its potential as a powerful
tool for dynamic mesh reconstruction. The code is publicly available at
https://github.com/yaoyx689/DynoSurf.