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.
AbstractWe 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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In October 2026, we discovered a related article by Shen et al. [2010]
that leverages the same physical analogy of a Faraday cage to determine a notion of interiority for imperfect boundaries.
We have corrected our work to include a reference and discussion of their approach.
It is particularly encouraging to us that others have independently recognized the utility of the
Faraday cage model for spatial partitioning and associated tasks. We hope that both their work and
our own will spur further investigation into the theoretical properties and practical benefits of
the Faraday cage perspective in a wide variety of modeling contexts.
Shen et al. [2010] are among the first to employ the Faraday cage model in computer graphics, using electric field strength as a measure of shielding to generate cleaned volumetric representations of triangle soups. Their setup models the equipotential constraint by constraining the domain boundary and input geometry to fixed function values. In contrast, our work leverages this same information for a different application: orientation and surfacing of point clouds. In addition to the field strength itself, our method uses the spatial variation of this electric field strength, and shows that it reliably offers orientation information even in concave (Figs. 3, 14) and interior (S4.1) regions when field strength is relatively uniform. Other contributions of our work include extensive validation across multiple categories of input, Dirichlet “sculpting” of the electromagnetic field (S3.6), and the formulation of the equipotential constraint (from Chapman et al. [2015]) which allows the cage value to float according to the exterior boundary conditions.