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1. Möbius Registration

   https://doi.org/10.1111/cgf.13503  

   Baden, Alex; Crane, Keenan; Kazhdan, Misha (July 2018, Computer graphics forum)

   Conformal parameterizations over the sphere provide high-quality maps between genus zero surfaces, and are essential for applications such as data transfer and comparative shape analysis. However, such maps are not unique: to define correspondence between two surfaces, one must find the Möbius transformation that best aligns two parameterizations—akin to picking a translation and rotation in rigid registration problems. We describe a simple procedure that canonically centers and rotationally aligns two spherical maps. Centering is implemented via elementary operations on triangle meshes in R3, and minimizes area distortion. Alignment is achieved using the FFT over the group of rotations. We examine this procedure in the context of spherical conformal parameterization, orbifold maps, non-rigid symmetry detection, and dense point-to-point surface correspondence. 

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2. Multiview Supervision By Registration

   Zhang, Y.; Park, H.S. (January 2020, IEEE Winter Conference on Applications of Computer Vision)

   This paper presents a semi-supervised learning framework to train a keypoint detector using multiview image streams given the limited number of labeled instances (typically <4%). We leverage three self-supervisionary signals in multiview tracking to utilize the unlabeled data: (1) a keypoint in one view can be supervised by other views via epipolar geometry; (2) a keypoint detection must be consistent across time; (3) a visible keypoint in one view is likely to be visible in the adjacent view. We design a new end-toend network that can propagate these self-supervisionary signals across the unlabeled data from the labeled data in a differentiable manner. We show that our approach outperforms existing detectors including DeepLabCut tailored to the keypoint detection of non-human species such as monkeys, dogs, and mice. 

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3. Nonparametric continuous sensor registration

   Clark, W.; Ghaffari, M.; Bloch, A. (January 2021, Journal of machine learning research)
4. Towards Saner Deep Image Registration

   Duan, Bin; Zhong, Ming; Yan, Yan (January 2023, International Conference on Computer Vision)
5. Metric Learning for Image Registration

   Niethammer, M.; Kwitt, R.; Vialard, F.-X. (January 2019, Computer Vision and Pattern Recognition (CVPR))