A Geometric Gaussian Mixture Representation of Plane Curves

• URL: http://arxiv.org/abs/2606.06505v1
• Date: Sun, 24 May 2026 16:19:27 GMT
• Title: A Geometric Gaussian Mixture Representation of Plane Curves
• Abstract summary: We introduce a user defined probabilistic polygonal representation for plane curves.<n>Each segment is equipped with a user defined uncertainty parameter in the normal direction.<n>Experiments on a collection of canonical plane curves show that the resulting GMM capture local tangent, local normal, and local arc length.
• Score: 16.543301505319103
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a user defined probabilistic polygonal representation for plane curves. Given a curve, we select vertices on the curve and connect consecutive vertices by line segments to obtain a polygonal approximation. Each segment is equipped with a user defined uncertainty parameter in the normal direction. This yields a collection of thin probabilistic geometric primitives that retain the geometrz of the underlying curve while extending it beyond the idealized deterministic one dimensional formulation. For each segment, we define a Random Variable that is uniform distributed in the tangent direction of the segment and Gaussian distributed in the normal direction of the segment. By matching the first and the second central moments, this construction induces a Gaussian component whose mean lies at the segment midpoint and whose covariance encodes both tangential and normal uncertainty. Combining the segment wise components with appropriate weights yields a Gaussian Mixture Model (GMM) representation of the user defined probabilistic polygonal representation of the plane curve. The proposed framework provides an analytically tractable probabilistic model that preserves local geometry, and uncertainty in the normal direction. It applies to smooth, closed, open, non regular, and self intersecting plane curves, allows adaptive discretization and varying uncertainty in the normal direction, and as a result supports uncertainty aware geometric modeling. Experiments on a collection of canonical plane curves show that the resulting GMM capture local tangent, local normal, and local arc length; resulting in the global shape of the underlying curves to be truthfully captured as well. The representation is particularly relevant for applications in uncertainty aware CAD and digital twins, probabilistic obstacle modeling in robotics, and probabilistic trajectory planning.

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