RFEPS: Reconstructing Feature-line Equipped Polygonal Surface
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Abstract (arXiv)
Feature lines are important geometric cues in characterizing the structure of a CAD model. Despite great progress in both explicit reconstruction and implicit reconstruction, it remains a challenging task to reconstruct a polygonal surface equipped with feature lines, especially when the input point cloud is noisy and lacks faithful normal vectors. In this paper, we develop a multistage algorithm, named RFEPS, to address this challenge. The key steps include (1)denoising the point cloud based on the assumption of local planarity, (2)identifying the feature-line zone by optimization of discrete optimal transport, (3)augmenting the point set so that sufficiently many additional points are generated on potential geometry edges, and (4) generating a polygonal surface that interpolates the augmented point set based on restricted power diagram. We demonstrate through extensive experiments that RFEPS, benefiting from the edge-point augmentation and the feature-preserving explicit reconstruction, outperforms state-of-the-art methods in terms of the reconstruction quality, especially in terms of the ability to reconstruct missing feature lines.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Dec 2022.
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Citing papers per year: 2023: 1; 2024: 3; 2025: 1; 2026: 2.
7 papers.
- 2023Neural-Singular-Hessian: Implicit Neural Representation of Unoriented Point Clouds by Enforcing Singular Hessiancited by 34
- 2024NeurCADRecon: Neural Representation for Reconstructing CAD Surfaces by Enforcing Zero Gaussian Curvaturecited by 12
- 2025CLR-Wire: Towards Continuous Latent Representations for 3D Curve Wireframe Generationcited by 7
- 2024Consistent Point Orientation for Manifold Surfaces via Boundary Integrationcited by 4
- 2026Autoregressive B-Rep Shape Generation with Parametric Surfacescited by 2
- 2024Neural Octahedral Field: Octahedral prior for simultaneous smoothing and sharp edge regularizationcited by 0
- 2026SharpNet: Enhancing MLPs to Represent Functions with Controlled Non-differentiabilitycited by 0
- 2026Autoregressive B-Rep Shape Generation with Parametric Surfaces19 Jul 2026
- 2026SharpNet: Enhancing MLPs to Represent Functions with Controlled Non-differentiability27 Jan 2026
- 2025CLR-Wire: Towards Continuous Latent Representations for 3D Curve Wireframe Generation27 Apr 2025
- 2024Neural Octahedral Field: Octahedral prior for simultaneous smoothing and sharp edge regularization1 Aug 2024
- 2024Consistent Point Orientation for Manifold Surfaces via Boundary Integration3 Jul 2024
- 2024NeurCADRecon: Neural Representation for Reconstructing CAD Surfaces by Enforcing Zero Gaussian Curvature20 Apr 2024
- 2023Neural-Singular-Hessian: Implicit Neural Representation of Unoriented Point Clouds by Enforcing Singular Hessian4 Sep 2023