WikiSysop: Created page with "== Citation == X. Zeng and M. Xu, “Gum-net: Unsupervised geometric matching for fast and accurate 3d subtomogram image alignment and averaging,” in Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 4073–4084. == Abstract == We propose a Geometric unsupervised matching Net- work (Gum-Net) for finding the geometric correspondence between two images with application to 3D subtomogram alignment and averaging. Subtomogram alignmen..."

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Created page with "== Citation == X. Zeng and M. Xu, “Gum-net: Unsupervised geometric matching for fast and accurate 3d subtomogram image alignment and averaging,” in Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 4073–4084. == Abstract == We propose a Geometric unsupervised matching Net- work (Gum-Net) for finding the geometric correspondence between two images with application to 3D subtomogram alignment and averaging. Subtomogram alignmen..."

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== Citation ==

X. Zeng and M. Xu, “Gum-net: Unsupervised geometric matching for fast and accurate 3d subtomogram image alignment and averaging,” in Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 4073–4084.

== Abstract ==

We propose a Geometric unsupervised matching Net-
work (Gum-Net) for finding the geometric correspondence
between two images with application to 3D subtomogram
alignment and averaging. Subtomogram alignment is the
most important task in cryo-electron tomography (cryo-ET),
a revolutionary 3D imaging technique for visualizing the
molecular organization of unperturbed cellular landscapes
in single cells. However, subtomogram alignment and av-
eraging are very challenging due to severe imaging limits
such as noise and missing wedge effects. We introduce an
end-to-end trainable architecture with three novel modules
specifically designed for preserving feature spatial informa-
tion and propagating feature matching information. The
training is performed in a fully unsupervised fashion to op-
timize a matching metric. No ground truth transformation
information nor category-level or instance-level matching
supervision information is needed. After systematic assess-
ments on six real and nine simulated datasets, we demon-
strate that Gum-Net reduced the alignment error by 40 to
50% and improved the averaging resolution by 10%. Gum-
Net also achieved 70 to 110 times speedup in practice with
GPU acceleration compared to state-of-the-art subtomo-
gram alignment methods. Our work is the first 3D unsu-
pervised geometric matching method for images of strong
transformation variation and high noise level. The train-
ing code, trained model, and datasets are available in our
open-source software AITom.

== Keywords ==

== Links ==

https://openaccess.thecvf.com/content_CVPR_2020/html/Zeng_Gum-Net_Unsupervised_Geometric_Matching_for_Fast_and_Accurate_3D_Subtomogram_CVPR_2020_paper.html

== Related software ==

== Related methods ==

== Comments ==

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