[jeffchuber]

This is a great overview. Also checkout CS 468 from Stanford, http://graphics.stanford.edu/courses/cs468-17-spring/ "Machine Learning for 3D Data"

Also, if you want to work on this stuff full time- https://news.ycombinator.com/item?id=17649726

[bitL]

Super cool course! Thanks for the link!

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.

[gtmtg]

(I wrote the original article)

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!

[jcims]

Any idea if the iPhone X surfaces RGBD from the TrueDepth camera?

[gtmtg]

Yes! Haven’t had a chance to play around with it but I’ve been wanting to. See AVDepthData: docs at https://developer.apple.com/documentation/avfoundation/avdep... and reference implementation for streaming depth at https://developer.apple.com/documentation/avfoundation/camer...

[jeffchuber]

Yes

[MrQuincle]

Thanks for the article. It is well written.

You promise some directions for fruitful research. It is a bit light on that. Maybe it's nice to expand on that topic a bit more.

[jeffchuber]

Not workable for a self-driving car - but for other applications the iPhone X has a front-facing RGB-D sensor.

[a_t48]

As an alternative, dumping out pixel ground truth from something like Unreal Engine isn’t hard to do.