Learning Bijective Surface Parameterization for Inferring Signed Distance Functions from Sparse Point Clouds with Grid Deformation

Takeshi Noda, Chao Chen, Junsheng Zhou, Weiqi Zhang, Yu-Shen Liu, Zhizhong Han

CVPR 2025 pp. 22139-22149

doi:10.1109/CVPR52734.2025.02062 /cvpr/2025/noda2025cvpr-learning/

Abstract

Inferring signed distance functions (SDFs) from sparse point clouds remains a challenge in surface reconstruction. The key lies in the lack of detailed geometric information in sparse point clouds, which is essential for learning a continuous field. To resolve this issue, we present a novel approach that learns a dynamic deformation network to predict SDFs in an end-to-end manner. To parameterize a continuous surface from sparse points, we propose a bijective surface parameterization (BSP) that learns the global shape from local patches. Specifically, we construct a bijective mapping for sparse points from the parametric domain to 3D local patches, integrating patches into the global surface. Meanwhile, we introduce grid deformation optimization (GDO) into the surface approximation to optimize the deformation of grid points and further refine the parametric surfaces. Experimental results on synthetic and real scanned datasets demonstrate that our method significantly outperforms the current state-of-the-art methods.

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Cite

Text

Noda et al. "Learning Bijective Surface Parameterization for Inferring Signed Distance Functions from Sparse Point Clouds with Grid Deformation." Conference on Computer Vision and Pattern Recognition, 2025. doi:10.1109/CVPR52734.2025.02062

Markdown

[Noda et al. "Learning Bijective Surface Parameterization for Inferring Signed Distance Functions from Sparse Point Clouds with Grid Deformation." Conference on Computer Vision and Pattern Recognition, 2025.](https://mlanthology.org/cvpr/2025/noda2025cvpr-learning/) doi:10.1109/CVPR52734.2025.02062

BibTeX

@inproceedings{noda2025cvpr-learning,
  title     = {{Learning Bijective Surface Parameterization for Inferring Signed Distance Functions from Sparse Point Clouds with Grid Deformation}},
  author    = {Noda, Takeshi and Chen, Chao and Zhou, Junsheng and Zhang, Weiqi and Liu, Yu-Shen and Han, Zhizhong},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2025},
  pages     = {22139-22149},
  doi       = {10.1109/CVPR52734.2025.02062},
  url       = {https://mlanthology.org/cvpr/2025/noda2025cvpr-learning/}
}