Robust Containment Queries over Collections of Rational Parametric Curves via Generalized Winding Numbers
Jacob Spainhour, David Gunderman, Kenneth Weiss
Abstract
Point containment queries for regions bound by watertight geometric surfaces, i.e., closed and without self-intersections, can be evaluated straightforwardly with a number of well-studied algorithms. When this assumption on domain geometry is not met, such methods are either unusable, or prone to misclassifications that can lead to cascading errors in downstream applications. More robust point classification schemes based on generalized winding numbers have been proposed, as they are indifferent to these imperfections. However, existing algorithms are limited to point clouds and collections of linear elements. We extend this methodology to encompass more general curved shapes with an algorithm that evaluates the winding number scalar field over unstructured collections of rational parametric curves. In particular, we evaluate the winding number for each curve independently, making the derived containment query robust to how the curves are arranged. We ensure geometric fidelity in our queries by treating each curve as equivalent to an adaptively constructed polyline that provably has the same generalized winding number at the point of interest. Our algorithm is numerically stable for points that are arbitrarily close to the model, and explicitly treats points that are coincident with curves. We demonstrate the improvements in computational performance granted by this method over conventional techniques as well as the robustness induced by its application.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 7ae4619c-c896-47fd-92e7-2d3bc23bca2dCited by top-tier papers4
- Lifting the Winding Number: Precise Discontinuities in Neural Fields for Physics SimulationYue Chang, Mengfei Liu, Zhecheng Wang, Peter Yichen Chen et al.SIGGRAPH 2025 · 4 citations
- The Antipodal Method: Fast, Accurate, and Robust 3D Generalized Winding NumbersCedric Martens, Philip Trettner, Mikhail BessmeltsevSIGGRAPH 2026
- Fast and Exact Winding Numbers for Triangle MeshesPeiyuan Xie, Christian Hafner, Chris WojtanSIGGRAPH 2026
- Spatially Accelerated Winding Numbers for Curved GeometryJacob Spainhour, Brad Whitlock, Kenneth WeissSIGGRAPH 2026
Builds on3
- Monte Carlo geometry processing: a grid-free approach to PDE-based methods on volumetric domainsRohan Sawhney, Keenan CraneSIGGRAPH 2020 · 99 citations
- EMBER: exact mesh booleans via efficient & robust local arrangementsPhilip Trettner, Julius Nehring-Wirxel, Leif KobbeltSIGGRAPH 2022 · 38 citations
- Polar stroking: new theory and methods for stroking pathsMark J. KilgardSIGGRAPH 2020 · 13 citations
Related papers
- Closed-form Generalized Winding Numbers of Rational Parametric Curves for Robust Containment QueriesShibo Liu, Ligang Liu, Xiao-Ming FuSIGGRAPH 2025 · 4 citations
- Winding Numbers on Discrete SurfacesNicole Feng, Mark Gillespie, Keenan CraneSIGGRAPH 2023 · 19 citations
- Leaps and Bounds: An Improved Point Cloud Winding Number Formulation for Fast Normal Estimation and Surface ReconstructionChamin Hewa Koneputugodage, Dylan Campbell, Stephen GouldICCV 2025 · 1 citation
- A Heat Method for Generalized Signed DistanceNicole Feng, Keenan CraneSIGGRAPH 2024 · 26 citations
- Consistent Point Orientation for Manifold Surfaces via Boundary IntegrationWeizhou Liu, Xingce Wang, Haichuan Zhao, Xingfei Xue et al.SIGGRAPH 2024 · 13 citations
