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Cited by 1023D Shape Modeling and AnalysisComputer Graphics and Visualization TechniquesImage Processing and 3D Reconstruction

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

P2P-NET: bidirectional point displacement net for shape transform

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Yin, Kangxue, Huang, Hui, Cohen-Or, Daniel, Zhang, Hao·2018·ACM Transactions on Graphics
DOI 10.1145/3197517.3201288
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Abstract

We introduce P2P-NET, a general-purpose deep neural network which learns geometric transformations between point-based shape representations from two domains, e.g., meso-skeletons and surfaces, partial and complete scans, etc. The architecture of the P2P-NET is that of a bi-directional point displacement network, which transforms a source point set to a prediction of the target point set with the same cardinality, and vice versa, by applying point-wise displacement vectors learned from data. P2P-NET is trained on paired shapes from the source and target domains, but without relying on point-to-point correspondences between the source and target point sets. The training loss combines two uni-directional geometric losses, each enforcing a shape-wise similarity between the predicted and the target point sets, and a cross-regularization term to encourage consistency between displacement vectors going in opposite directions. We develop and present several different applications enabled by our general-purpose bidirectional P2P-NET to highlight the effectiveness, versatility, and potential of our network in solving a variety of point-based shape transformation problems.

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  • Operating on the mesh graph yields significantly better performance compared to using alternative architectures that operate on point-sampled representations or volumetric representations.

    “As we show in our experiments, operating on the mesh graph yields significantly better performance compared to using alternative architectures that operate on point-sampled representations [Yin et al. 2018] or volumetric representations [Xu et al. 2019].”

    From RigNet: Neural Rigging for Articulated Characters (2020) · cites this paper as Yin et al. 2018

  • The displacement mapping is reminiscent of P2P-Net that learns to displace surface points across different domains e.g., surface points to meso-skeletons.

    “This mapping is reminiscent of P2P-Net [Yin et al. 2018] that learns to displace surface points across different domains e.g., surface points to meso-skeletons.”

    From RigNet: Neural Rigging for Articulated Characters (2020) · cites this paper as Yin et al. 2018

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Citations

Cites

  • Reconstruction and representation of 3D objects with radial basis functions

    Carr, J. C., Beatson, R. K., Cherrie, J. B., Mitchell, T. J., Fright, W. R., McCallum, B. C., & Evans, T. R. (2001) · Proceedings of the 28th annual conference on Computer graphics and interactive techniques · doi:10.1145/383259.383266 · Cited by 1,852 · cited 2×

    “aptured, accumulated, and processed by many useful techniques, e.g., in point cloud filtering [Mitra and Nguyen 2003], resampling [Huang et al. 2013b], and surface reconstruction [Berger et al. 2017; Carr et al. 2001; Kazhdan and Hoppe 2013], to name just a few. Point set displacement. Several past works took a displa…”
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Metadata

Authors
Yin, K., Huang, H., Cohen-Or, D., & Zhang, H.
Affiliations
  • Simon Fraser University (CA)
  • Shenzhen University (CN)
  • Tel Aviv University (IL)
ORCID
Zhang, Hao
Venue
ACM Transactions on Graphics
Publisher
Association for Computing Machinery (ACM)
Published
2018-07-30
Volume
37
Issue
4
Pages
1-13
ISSN
  • 0730-0301 (ISSN-L)
  • 1557-7368
DOI
10.1145/3197517.3201288
OpenAlex
W2794816985
Cited by
102
References
1
Citations by year
2026: 3 · 2025: 5 · 2024: 13 · 2023: 16 · 2022: 19 · 2021: 17 · 2020: 11 · 2019: 18
FWCI
8.29
Type
journal article
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • ACM Transactions on Graphics (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Natural Science Foundation of China
  • National Key Research and Development Program of China
Topics
  • Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › 3D Shape Modeling and Analysis
  • Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Computer Graphics and Visualization Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Processing and 3D Reconstruction
Keywords
partial scan, geometric loss

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