mlsanityData to 14 Oct 2026

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

arXiv 2503.23670 ↗First posted 31 Mar 2025cs.CVcited by 7

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Abstract (arXiv)

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. Project page: https://takeshie.github.io/Bijective-SDF

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 59 of its 75 references that are in the map. First posted on arXiv 31 Mar 2025.

Builds on

59 of 75 references.

References · 59

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38 more
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  19. 2024Neural Signed Distance Function Inference through Splatting 3D Gaussians Pulled on Zero-Level Setcited by 49
  20. 2022CAP-UDF: Learning Unsigned Distance Functions Progressively from Raw Point Clouds with Consistency-Aware Field Optimizationcited by 47
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50 most cited of 59 All 59 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 3.

7 papers.

  1. 2025MaterialRefGS: Reflective Gaussian Splatting with Multi-view Consistent Material Inferencecited by 23
  2. 2025SparseRecon: Neural Implicit Surface Reconstruction from Sparse Views with Feature and Depth Consistenciescited by 9
  3. 2025GAP: Gaussianize Any Point Clouds with Text Guidancecited by 7
  4. 2025U-CAN: Unsupervised Point Cloud Denoising with Consistency-Aware Noise2Noise Matchingcited by 3
  5. 2026SGAD-SLAM: Splatting Gaussians at Adjusted Depth for Better Radiance Fields in RGBD SLAMcited by 1
  6. 2026GaussianGrow: Geometry-aware Gaussian Growing from 3D Point Clouds with Text Guidancecited by 1
  7. 2026Learning Manifolds in High-D Point Embedding for Anisotropic Surface Approximation from Unstructured Point Cloudscited by 0