research-article

RFEPS: Reconstructing Feature-Line Equipped Polygonal Surface

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Changhe TuAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 41, Issue 6

Article No.: 228, Pages 1 - 15

https://doi.org/10.1145/3550454.3555443

Published: 30 November 2022 Publication History

Abstract

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.

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Chen TCao RLi ZZang YSun L(2024)Deep3DSketch-im: rapid high-fidelity AI 3D model generation by single freehand sketchesDeep3DSketch-im:基于人工智能从单个手绘草图快速生成高保真三维模型Frontiers of Information Technology & Electronic Engineering10.1631/FITEE.230031425:1(149-159)Online publication date: 8-Feb-2024
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Index Terms

RFEPS: Reconstructing Feature-Line Equipped Polygonal Surface
1. Computing methodologies
  1. Computer graphics
    1. Shape modeling

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 41, Issue 6

December 2022

1428 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3550454

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Published: 30 November 2022

Published in TOG Volume 41, Issue 6

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Chen TCao RLi ZZang YSun L(2024)Deep3DSketch-im: rapid high-fidelity AI 3D model generation by single freehand sketchesDeep3DSketch-im:基于人工智能从单个手绘草图快速生成高保真三维模型Frontiers of Information Technology & Electronic Engineering10.1631/FITEE.230031425:1(149-159)Online publication date: 8-Feb-2024
https://doi.org/10.1631/FITEE.2300314
Huang GFang QZhang ZLiu LFu X(2024)Stochastic Normal Orientation for Point CloudsACM Transactions on Graphics10.1145/368794443:6(1-12)Online publication date: 19-Dec-2024
https://dl.acm.org/doi/10.1145/3687944
Wang NHuang HSong SWang BWang WGuo X(2024)MATTopo: Topology-preserving Medial Axis Transform with Restricted Power DiagramACM Transactions on Graphics10.1145/368776343:6(1-16)Online publication date: 19-Dec-2024
https://dl.acm.org/doi/10.1145/3687763
Lv XHe ZYang YNie JZhang JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)SAR-SLAM: Self-Attentive Rendering-based SLAM with Neural Point Cloud EncodingProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680696(6222-6231)Online publication date: 28-Oct-2024
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