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Denoising Diffusion Probabilistic Models on SO(3) for Rotational Alignment

Leach, Adam and Schmon, Sebastian M. and Degiacomi, Matteo T. and Willcocks, Chris G. (2022) 'Denoising Diffusion Probabilistic Models on SO(3) for Rotational Alignment.', ICLR 2022 Workshop on Geometrical and Topological Representation Learning, 29 April 2022.


Probabilistic diffusion models are capable of modeling complex data distributions on high-dimensional Euclidean spaces for a range applications. However, many real world tasks involve more complex structures such as data distributions defined on manifolds which cannot be easily represented by diffusions on Rn. This paper proposes denoising diffusion models for tasks involving 3D rotations leveraging diffusion processes on the Lie group SO(3) in order to generate candidate solutions to rotational alignment tasks. The experimental results show the proposed SO(3) diffusion process outperforms na¨ıve approaches such as Euler angle diffusion in synthetic rotational distribution sampling and in a 3D object alignment task.

Item Type:Conference item (Paper)
Full text:(AM) Accepted Manuscript
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Date accepted:25 March 2022
Date deposited:24 June 2022
Date of first online publication:2022
Date first made open access:24 June 2022

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