Faraday Cage Estimation of Normals for Point Clouds and Ribbon Sketches

Daniel Scrivener1, Daniel Cui1, Ellis Coldren1, S. Mazdak Abulnaga2, Mikhail Bessmeltsev3, Edward Chien1
1Boston University, 2Massachusetts Institute of Technology / Harvard Medical School, 3Université de Montréal
ACM Transactions on Graphics (SIGGRAPH 2025)
Best Paper Award, Honorable Mention

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. Our method is uniquely robust to the presence of interior structures and artifacts, producing superior surfacing output when combined with Poisson Surface Reconstruction. Left: A VR ribbon sketch [Rosales et al. 2021, 2019] is sparsified to mimic a simpler, plausible user input, and points are evenly sampled from the ribbons. Middle: Points form a Faraday cage around the interior. Electric potentials under various linear external fields, and the maximum electric field strength over these scenarios are shown. Right: Gradient information from maximum field strength is used to estimate normals and filter interior parts of ribbons. Poisson Surface Reconstruction [Kazhdan and Hoppe 2013] is used to generate the surface free of interior structures and concavities at intersecting points.

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.

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BibTeX

@article{scrivener2025faraday, author = {Scrivener, Daniel and Cui, Daniel and Coldren, Ellis and Abulnaga, S. Mazdak and Bessmeltsev, Mikhail and Chien, Edward}, title = {Faraday Cage Estimation of Normals for Point Clouds and Ribbon Sketches}, year = {2025}, issue_date = {August 2025}, publisher = {Association for Computing Machinery}, volume = {44}, number = {4}, issn = {0730-0301}, doi = {10.1145/3731212}, journal = {ACM Trans. Graph.}, articleno = {49}, numpages = {13}, }