An Adaptive Lagrangian B-Spline Framework for Point Cloud Manifold Evolution
Muhammad Ammad, Leevan Ling

TL;DR
This paper introduces an adaptive, meshless B-spline framework for evolving point cloud surfaces in 3D, enabling high-order geometric analysis and dynamic surface deformation with improved accuracy and resolution.
Contribution
It extends previous curve-evolution methods to a localized, adaptive B-spline approach for 3D surface evolution directly from point clouds, incorporating error-guided refinement and control point updates.
Findings
Accurately reproduces mean-curvature flow and anisotropic deformations.
Maintains surface regularity through adaptive knot insertion and point redistribution.
Demonstrates efficiency and precision in dynamic manifold approximation.
Abstract
We extend our recent curve-evolution framework based on localized B-spline interpolation to present an adaptive Lagrangian framework for the geometric evolution of point-cloud data representing smooth, codimension-one surfaces in . The method constructs overlapping, localized tensor-product B-spline patches, enabling direct, meshless surface evolution from discrete samples. Within each patch, the differentiable B-spline representation yields analytic, high-order estimates of intrinsic geometric invariants, supporting curvature-driven and geometry-coupled flows. The organization of control points facilitates coherent updates of both surface samples and spline coefficients under intrinsic velocity fields. A conditioning-aware formulation of the local interpolation system, combined with a Gauss-Seidel refinement of control points, maintains interpolation quality throughout…
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Taxonomy
TopicsAdvanced Numerical Analysis Techniques · Model Reduction and Neural Networks · 3D Shape Modeling and Analysis
