DynoSurf: Neural Deformation-based Temporally Consistent Dynamic Surface Reconstruction
<|reference_start|>DynoSurf: Neural Deformation-based Temporally Consistent Dynamic Surface Reconstruction: 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.<|reference_end|>
@article{yao2024dynosurf:,
    title={DynoSurf: Neural Deformation-based Temporally Consistent Dynamic Surface
  Reconstruction},
    author={Yuxin Yao, Siyu Ren, Junhui Hou, Zhi Deng, Juyong Zhang, Wenping Wang},
    journal={arXiv preprint arXiv:2403.11586},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.11586},
    primaryClass={cs.CV}
}