Linear Spherical Sliced Optimal Transport: A Fast Metric for Comparing Spherical Data

• URL: http://arxiv.org/abs/2411.06055v1
• Date: Sat, 09 Nov 2024 03:36:59 GMT
• Title: Linear Spherical Sliced Optimal Transport: A Fast Metric for Comparing Spherical Data
• Authors: Xinran Liu, Yikun Bai, Rocío Díaz Martín, Kaiwen Shi, Ashkan Shahbazi, Bennett A. Landman, Catie Chang, Soheil Kolouri,
• Abstract summary: linear optimal transport has been proposed to embed distributions into ( L2 ) spaces, where the ( L2 ) distance approximates the optimal transport distance. We introduce the Linear Spherical Sliced Optimal Transport framework, which embeds spherical distributions into ( L2 ) spaces while preserving their intrinsic geometry. We demonstrate its superior computational efficiency in applications such as cortical surface registration, 3D point cloud via flow gradient, and shape embedding.
• Score: 11.432994366694862
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• Abstract: Efficient comparison of spherical probability distributions becomes important in fields such as computer vision, geosciences, and medicine. Sliced optimal transport distances, such as spherical and stereographic spherical sliced Wasserstein distances, have recently been developed to address this need. These methods reduce the computational burden of optimal transport by slicing hyperspheres into one-dimensional projections, i.e., lines or circles. Concurrently, linear optimal transport has been proposed to embed distributions into \( L^2 \) spaces, where the \( L^2 \) distance approximates the optimal transport distance, thereby simplifying comparisons across multiple distributions. In this work, we introduce the Linear Spherical Sliced Optimal Transport (LSSOT) framework, which utilizes slicing to embed spherical distributions into \( L^2 \) spaces while preserving their intrinsic geometry, offering a computationally efficient metric for spherical probability measures. We establish the metricity of LSSOT and demonstrate its superior computational efficiency in applications such as cortical surface registration, 3D point cloud interpolation via gradient flow, and shape embedding. Our results demonstrate the significant computational benefits and high accuracy of LSSOT in these applications.

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