🤖 AI Summary
Existing linear methods for computing discrete geodesic distances often suffer from artifacts and insufficient accuracy on non-uniform or coarse meshes. This work proposes a geodesic distance representation and optimization framework based on piecewise quadratic finite elements, which, for the first time, employs a second-order representation to exactly reproduce geodesic distances in flat regions without relying on mesh quality. The approach naturally accommodates point or curve sources at arbitrary locations—including non-vertex positions—and seamlessly integrates discrete differential geometry with geodesic distance optimization. Extensive evaluations demonstrate that the method significantly enhances both accuracy and robustness across a variety of triangular meshes, particularly excelling on coarse or highly non-uniform discretizations.
📝 Abstract
We introduce a novel representation and optimization framework for discrete geodesics on triangle meshes that reduces artifacts of linear methods on uneven and coarse discretizations. Our method computes squared geodesic distances from point and curve sources using piecewise-quadratic elements, exactly reproducing flat distances regardless of mesh quality while improving accuracy over existing approaches on curved meshes. The formulation naturally supports sources placed anywhere on the mesh, not just at vertices.