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Yes. NERF estimates both volume and color for a point in space so by stepping through the volume at a certain resolution you can extract the mesh. The crazy thing is that although the NERF paper was only published in 2020 it's already old hat as it inspired a flood of papers which came after it. See https://github.com/yenchenlin/awesome-NeRF. One of the most interesting papers I think is https://alexyu.net/plenoxels/?s=09 which talks about building radiance fields with just classical optimization, i.e. no neural nets and which claims to be much faster to converge i.e. minutes vs days. Not used in any photogrammetry software yet as far as I know but potentially exciting for the future.
Start with this: https://alexyu.net/plenoxels/ and their github: https://github.com/sxyu/svox2 It has everything, including calling COLMAP to build initial sparse point cloud.
As an update on this one, research seems to be moving incredibly quickly and another method for really quick training of NERF networks has been released https://github.com/NVlabs/instant-ngp You need a beefy GPU to run this but the results are amazing (it can reconstruct shiny, and to an extent translucent surfaces) so I'm excited to see where this goes in the future.