Correspondence-Free Fast and Robust Spherical Point Pattern Registration

• URL: http://arxiv.org/abs/2508.02339v1
• Date: Mon, 04 Aug 2025 12:21:05 GMT
• Title: Correspondence-Free Fast and Robust Spherical Point Pattern Registration
• Authors: Anik Sarker, Alan T. Asbeck,
• Abstract summary: We introduce three novel algorithms for estimation between two spherical patterns.<n>Our algorithms are over 10x faster and over 10x more accurate than current state-of-the-art methods for the Wahba problem with outliers.
• Score: 0.8287206589886879
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing methods for rotation estimation between two spherical ($\mathbb{S}^2$) patterns typically rely on spherical cross-correlation maximization between two spherical function. However, these approaches exhibit computational complexities greater than cubic $O(n^3)$ with respect to rotation space discretization and lack extensive evaluation under significant outlier contamination. To this end, we propose a rotation estimation algorithm between two spherical patterns with linear time complexity $O(n)$. Unlike existing spherical-function-based methods, we explicitly represent spherical patterns as discrete 3D point sets on the unit sphere, reformulating rotation estimation as a spherical point-set alignment (i.e., Wahba problem for 3D unit vectors). Given the geometric nature of our formulation, our spherical pattern alignment algorithm naturally aligns with the Wahba problem framework for 3D unit vectors. Specifically, we introduce three novel algorithms: (1) SPMC (Spherical Pattern Matching by Correlation), (2) FRS (Fast Rotation Search), and (3) a hybrid approach (SPMC+FRS) that combines the advantages of the previous two methods. Our experiments demonstrate that in the $\mathbb{S}^2$ domain and in correspondence-free settings, our algorithms are over 10x faster and over 10x more accurate than current state-of-the-art methods for the Wahba problem with outliers. We validate our approach through extensive simulations on a new dataset of spherical patterns, the ``Robust Vector Alignment Dataset. "Furthermore, we adapt our methods to two real-world tasks: (i) Point Cloud Registration (PCR) and (ii) rotation estimation for spherical images.

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