FastGeodis: Fast Generalised Geodesic Distance Transform
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Abstract (arXiv)
The FastGeodis package provides an efficient implementation for computing Geodesic and Euclidean distance transforms (or a mixture of both), targeting efficient utilisation of CPU and GPU hardware. In particular, it implements the paralellisable raster scan method from Criminisi et al. (2009), where elements in a row (2D) or plane (3D) can be computed with parallel threads. This package is able to handle 2D as well as 3D data, where it achieves up to a 20x speedup on a CPU and up to a 74x speedup on a GPU as compared to an existing open-source library (Wang, 2020) that uses a non-parallelisable single-thread CPU implementation. The performance speedups reported here were evaluated using 3D volume data on an Nvidia GeForce Titan X (12 GB) with a 6-Core Intel Xeon E5-1650 CPU. Further in-depth comparison of performance improvements are discussed in the FastGeodis documentation: https://fastgeodis.readthedocs.io
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 3 of its 11 references that are in the map. First posted on arXiv 26 Jul 2022.
Builds on
3 of 11 references.
References · 3
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Citing papers per year: 2023: 3; 2024: 3; 2025: 1; 2026: 1.
8 papers.
- 2024TopoCellGen: Generating Histopathology Cell Topology with a Diffusion Modelcited by 16
- 2025HiMo: High-Speed Objects Motion Compensation in Point Cloudscited by 12
- 2023Guiding the Guidance: A Comparative Analysis of User Guidance Signals for Interactive Segmentation of Volumetric Imagescited by 5
- 2024SiNGR: Brain Tumor Segmentation via Signed Normalized Geodesic Transform Regressioncited by 4
- 2023Adaptive Multi-scale Online Likelihood Network for AI-assisted Interactive Segmentationcited by 3
- 2023Leveraging point annotations in segmentation learning with boundary losscited by 2
- 2024Scribble-Based Interactive Segmentation of Medical Hyperspectral Imagescited by 0
- 2026SE(3) Neural Potential Fields for 6-DoF Trajectory Planning Directly from Images Without Explicit 3D Reconstructioncited by 0
- 2026SE(3) Neural Potential Fields for 6-DoF Trajectory Planning Directly from Images Without Explicit 3D Reconstruction21 Sep 2026
- 2025HiMo: High-Speed Objects Motion Compensation in Point Clouds2 Mar 2025
- 2024TopoCellGen: Generating Histopathology Cell Topology with a Diffusion Model8 Dec 2024
- 2024Scribble-Based Interactive Segmentation of Medical Hyperspectral Images5 Aug 2024
- 2024SiNGR: Brain Tumor Segmentation via Signed Normalized Geodesic Transform Regression27 May 2024
- 2023Leveraging point annotations in segmentation learning with boundary loss6 Nov 2023
- 2023Adaptive Multi-scale Online Likelihood Network for AI-assisted Interactive Segmentation23 Mar 2023
- 2023Guiding the Guidance: A Comparative Analysis of User Guidance Signals for Interactive Segmentation of Volumetric Images13 Mar 2023