Reproduce “Gravity-aligned Rotation Averaging with Circular Regression” on OpenResearch
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About this paper
Linfei Pan, Marc Pollefeys, Dániel Baráth
Researchers from ETH Zurich and Microsoft developed a method for gravity-aligned rotation averaging that transforms the 3-degrees-of-freedom problem into a 1-degree-of-freedom circular regression. This approach enhances the accuracy of global Structure-from-Motion to state-of-the-art levels on diverse datasets while achieving up to an 8x speedup over prior global methods.
Key insights
- Leveraging readily available gravity information from IMUs can simplify the complex 3-DoF rotation averaging problem to a more tractable and efficient 1-DoF optimization.
- Circular regression offers a robust and principled way to handle the periodicity inherent in 1-DoF angular variables, outperforming heuristic approaches.
- Integrating gravity direction not only boosts accuracy in global SfM to state-of-the-art levels but also enables significant speedups.
Summary by alphaXiv.
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