In addition to the architectures mentioned in this great overview, I'm excited to see progress on spectral and geodesic CNNs for graphs and manifolds. Check out this other fantastic source for info on 3D ML: http://geometricdeeplearning.com
Also, if you want to work on this stuff full time- https://news.ycombinator.com/item?id=17649726
Do you know what are the most precise programmable RGB-D cameras a non-professional can buy? I was trying to extract 3D information just from a single camera via 3D convolutions and RNNs (for a self-driving car project) and would like to play with real 3D a bit as well.
I've been playing around with a few and I'd recommend the Orbbec Astra and the Intel RealSense (the new D435 is what I've been using) as decent but cheap cameras if you want to get started! The Asus Xtion PRO LIVE is also quite good but since it's been discontinued it's pretty hard to find.
The Stereolabs ZED relies on stereo vision but produces a similar output as traditional RGB-D cameras, and I've heard good things about it as well!
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_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
[1] Web-browser demo: https://storage.googleapis.com/tfjs-models/demos/posenet/cam... [2] Github: https://github.com/tensorflow/tfjs-models/tree/master/posene...
