Summary of Linear Spherical Sliced Optimal Transport: a Fast Metric For Comparing Spherical Data, by Xinran Liu et al.
Linear Spherical Sliced Optimal Transport: A Fast Metric for Comparing Spherical Data
by Xinran Liu, Yikun Bai, Rocío Díaz Martín, Kaiwen Shi, Ashkan Shahbazi, Bennett A. Landman, Catie Chang, Soheil Kolouri
First submitted to arxiv on: 9 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Metric Geometry (math.MG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel framework for efficiently comparing spherical probability distributions is introduced, with potential applications in computer vision, geosciences, medicine, and other fields. The Linear Spherical Sliced Optimal Transport (LSSOT) approach embeds spherical distributions into L2 spaces while preserving their intrinsic geometry, reducing the computational burden of optimal transport methods. By slicing hyperspheres into one-dimensional projections, LSSOT provides a computationally efficient metric for spherical probability measures, outperforming existing methods in applications such as cortical surface registration, 3D point cloud interpolation, and shape embedding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates a new way to compare shapes that are like balls. It’s useful for things like understanding brain surfaces, putting together 3D pictures, and comparing body parts. The new method is faster and better than what we had before, which makes it important for scientists who work with these kinds of data. |
Keywords
» Artificial intelligence » Embedding » Probability
