Faraday Cage Estimation of Normals for Point Clouds and Ribbon Sketches
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3731212.pdf
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Author(s)
Scrivener, Daniel
Cui, Daniel
Coldren, Ellis
Abulnaga, Mazdak
Bessmeltsev, Mikhail
Chien, Edward
Date Issued
July 25, 2025
Journal
ACM Transactions on Graphics
Publisher
ACM
Citation
Daniel Scrivener, Daniel Cui, Ellis Coldren, S. Mazdak Abulnaga, Mikhail Bessmeltsev, and Edward Chien. 2025. Faraday Cage Estimation of Normals for Point Clouds and Ribbon Sketches. ACM Trans. Graph. 44, 4, Article 49 (August 2025), 13 pages.
Version
Final published version
Abstract
We propose a novel method (FaCE) for normal estimation of unoriented point clouds and VR ribbon sketches that leverages a modeling of the Faraday cage effect. Input points, or a sampling of the ribbons, form a conductive cage and shield the interior from external fields. The gradient of the maximum field strength over external field scenarios is used to estimate a normal at each input point or ribbon. The electrostatic effect is modeled with a simple Poisson system, accommodating intuitive user-driven sculpting via the specification of point charges and Faraday cage points. On inputs sampled from clean, watertight meshes, our method achieves comparable normal quality to existing methods tailored for this scenario. On inputs containing interior structures and artifacts, our method produces superior surfacing output when combined with Poisson Surface Reconstruction. In the case of ribbon sketches, our method accommodates sparser ribbon input while maintaining an accurate geometry, allowing for greater flexibility in the artistic process. We demonstrate superior performance to an existing approach for surfacing ribbon sketches in this sparse setting.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1145/3731212
