WikiSysop: Created page with "== Citation == G. Woollard et al., “InstaMap: instant-NGP for cryo-EM density maps,” Biological Crystallography, vol. 81, no. 4, 2025. == Abstract == Despite the parallels between problems in computer vision and cryo-electron microscopy (cryo-EM), many state-of-the-art approaches from computer vision have yet to be adapted for cryo-EM. Within the computer-vision research community, implicits such as neural radiance fields (NeRFs) have enabled the detailed reconstr..."

2025-04-16T10:43:13Z

Created page with "== Citation == G. Woollard et al., “InstaMap: instant-NGP for cryo-EM density maps,” Biological Crystallography, vol. 81, no. 4, 2025. == Abstract == Despite the parallels between problems in computer vision and cryo-electron microscopy (cryo-EM), many state-of-the-art approaches from computer vision have yet to be adapted for cryo-EM. Within the computer-vision research community, implicits such as neural radiance fields (NeRFs) have enabled the detailed reconstr..."

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== Citation ==

G. Woollard et al., “InstaMap: instant-NGP for cryo-EM density maps,” Biological Crystallography, vol. 81, no. 4, 2025.

== Abstract ==

Despite the parallels between problems in computer vision and cryo-electron
microscopy (cryo-EM), many state-of-the-art approaches from computer vision
have yet to be adapted for cryo-EM. Within the computer-vision research
community, implicits such as neural radiance fields (NeRFs) have enabled the
detailed reconstruction of 3D objects from few images at different cameraviewing
angles. While other neural implicits, specifically density fields, have been
used to map conformational heterogeneity from noisy cryo-EM projection
images, most approaches represent volume with an implicit function in Fourier
space, which has disadvantages compared with solving the problem in real space,
complicating, for instance, masking, constraining physics or geometry, and
assessing local resolution. In this work, we build on a recent development in
neural implicits, a multi-resolution hash-encoding framework called instant-
NGP, that we use to represent the scalar volume directly in real space and apply
it to the cryo-EM density-map reconstruction problem (InstaMap). We
demonstrate that for both synthetic and real data, InstaMap for homogeneous
reconstruction achieves higher resolution at shorter training stages than five
other real-spaced representations. We propose a solution to noise overfitting,
demonstrate that InstaMap is both lightweight and fast to train, implement
masking from a user-provided input mask and extend it to molecular-shape
heterogeneity via bending space using a per-image vector field.

== Keywords ==

== Links ==

https://journals.iucr.org/d/issues/2025/04/00/sor5003/index.html

== Related software ==

== Related methods ==

== Comments ==

2025Woollard InstaMap - Revision history