Don't think it's exactly what you're talking about (I'm sure there are other works much closer to what you have in mind, just can't recall off the top of my hea

Don't think it's exactly what you're talking about (I'm sure there are other works much closer to what you have in mind, just can't recall off the top of my head) — but you might find PoseNet (https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Kendall_PoseNet_A_Convolutional_ICCV_2015_paper.pdf https://www.cv-foundation.org/openaccess/content_iccv_2015/p...) interesting. Not explicitly 3D, but estimates where in a large-scale scene a picture was taken using an end-to-end convolutional network. With that said, I think there's still a ton of merit in classical geometric approaches like ICP — there's a real, geometric basis to why they work. Convolutional networks can demonstrate some pretty amazing results, but they're still mostly "black boxes" to us, and a consequence of this is that it's hard to understand why they work and predict when they'll fail. This blog post (by the PoseNet author, actually) articulates the viewpoint well: https://alexgkendall.com/computer_vision/have_we_forgotten_about_geometry_in_computer_vision https://alexgkendall.com/computer_vision/have_we_forgotten_a.... One recent research direction that I personally find really fascinating is designing deep learning architectures around real geometric properties, e.g. as in Skydio's deep stereo work: https://arxiv.org/pdf/1703.04309.pdf https://arxiv.org/pdf/1703.04309.pdf