ACM Transactions on Graphics · 2010 · 181 citations · 16 references
ComputingComputer ScienceComputational GeometryGeometric ModelingGeometryCurvature AnalysisDistance MeasurementGeometry ProcessingMetric GeometryShape AnalysisShape MetricsCurvature EstimationDistance In Three-dimensional SpaceDistance MetricsShape
Measuring distances between points on a 3D surface is fundamental in computer graphics, yet existing metrics such as geodesic and diffusion distances fail to satisfy key properties like smoothness, shape‑awareness, and noise robustness. This work introduces a biharmonic‑based distance that satisfies all desired properties for surface distance metrics. The distance is defined by weighting Laplace‑Beltrami eigenvalues with inverse squares, yielding a trade‑off between near‑geodesic behavior for short paths and global shape‑awareness for long paths, and is validated theoretically and empirically on many meshes.
Measuring distances between pairs of points on a 3D surface is a fundamental problem in computer graphics and geometric processing. For most applications, the important properties of a distance are that it is a metric, smooth, locally isotropic, globally “shape-aware,” isometry-invariant, insensitive to noise and small topology changes, parameter-free, and practical to compute on a discrete mesh. However, the basic methods currently popular in computer graphics (e.g., geodesic and diffusion distances) do not have these basic properties. In this article, we propose a new distance measure based on the biharmonic differential operator that has all the desired properties. This new surface distance is related to the diffusion and commute-time distances, but applies different (inverse squared) weighting to the eigenvalues of the Laplace-Beltrami operator, which provides a nice trade-off between nearly geodesic distances for small distances and global shape-awareness for large distances. The article provides theoretical and empirical analysis for a large number of meshes.
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