A Survey of Algorithms for Geodesic Paths and Distances
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Abstract (arXiv)
Numerical computation of shortest paths or geodesics on curved domains, as well as the associated geodesic distance, arises in a broad range of applications across digital geometry processing, scientific computing, computer graphics, and computer vision. Relative to Euclidean distance computation, these tasks are complicated by the influence of curvature on the behavior of shortest paths, as well as the fact that the representation of the domain may itself be approximate. In spite of the difficulty of this problem, recent literature has developed a wide variety of sophisticated methods that enable rapid queries of geodesic information, even on relatively large models. This survey reviews the major categories of approaches to the computation of geodesic paths and distances, highlighting common themes and opportunities for future improvement.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jul 2020.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 1; 2025: 3; 2026: 4.
11 papers.
- 2022GraphWalks: Efficient Shape Agnostic Geodesic Shortest Path Estimationcited by 8
- 2023Learning the Geodesic Embedding with Graph Neural Networkscited by 8
- 2022Monitoring of Pigmented Skin Lesions Using 3D Whole Body Imagingcited by 4
- 2025Using the Path of Least Resistance to Explain Deep Networkscited by 3
- 2025Leveraging Point Transformers for Detecting Anatomical Landmarks in Digital Dentistrycited by 2
- 2026Parallelised Differentiable Straightest Geodesics for 3D Meshescited by 2
- 2025Registration-Free Monitoring of Unstructured Point Cloud Data via Intrinsic Geometrical Propertiescited by 1
- 2026From Extrinsic to Intrinsic: Geodesic-Guided Representation Learning for 3D Geometric Datacited by 1
- 2024A Spectral Framework for Evaluating Geodesic Distances Between Graphscited by 0
- 2026Manifold-Preserving Superpixel Hierarchies and Embeddings for the Exploration of High-Dimensional Imagescited by 0
- 2026Curve Skeletonization in Continuous domain for Meshes and Point Cloudscited by 0
- 2026From Extrinsic to Intrinsic: Geodesic-Guided Representation Learning for 3D Geometric Data1 Jun 2026
- 2026Curve Skeletonization in Continuous domain for Meshes and Point Clouds25 May 2026
- 2026Parallelised Differentiable Straightest Geodesics for 3D Meshes16 Mar 2026
- 2026Manifold-Preserving Superpixel Hierarchies and Embeddings for the Exploration of High-Dimensional Images27 Feb 2026
- 2025Registration-Free Monitoring of Unstructured Point Cloud Data via Intrinsic Geometrical Properties6 Nov 2025
- 2025Leveraging Point Transformers for Detecting Anatomical Landmarks in Digital Dentistry15 Apr 2025
- 2025Using the Path of Least Resistance to Explain Deep Networks17 Feb 2025
- 2024A Spectral Framework for Evaluating Geodesic Distances Between Graphs15 Jun 2024
- 2023Learning the Geodesic Embedding with Graph Neural Networks11 Sep 2023
- 2022GraphWalks: Efficient Shape Agnostic Geodesic Shortest Path Estimation30 May 2022
- 2022Monitoring of Pigmented Skin Lesions Using 3D Whole Body Imaging14 May 2022