For example, I predict stereo vision algorithms will die out soon, including deep-learning-assisted stereo vision. It's useful for now but not something to build a business around. Better time-of-flight depth cameras will be here soon enough. It's just basic physics. I worked on one for my PhD research. You can get pretty clean depth data with some basic statistics and no AI algorithm wizardry. We're just waiting for someone to take it to a fab, build driver electronics, and commercialize it.
This argument sounds like a second cousin of the "chemicals are bad, but if it is natural it is good" argument. Just because it evolved in nature doesn't mean it's optimal, or it's the best system under massively different constraints. And who knows what evolution would have thrown up after a few more billion years.
The distance at which a stereo vision system can capture precise depths depends on the distance between eyes, and the eyes' angular resolution. Human depth perception works well for things within about 10m, but when you get out to 20-40m humans get a lot less info from stereo vision.
When you get to that distance, humans seem to have a whole load of different tricks - shadows, rate of size change, recognising things of known size, perspective and so on. You can see a car and know how far it is even without stereo vision, because you know how big cars are, and how big lanes and road markings are. You can even see two red lights in the distance at night and work out whether they're the two corners of a car, or two motorbikes side-by-side and closer to you.
On the other hand, your basic general-purpose stereo machine vision system doesn't try to understand what it's looking at - you just identify 'landmarks' that can be matched in both images (high contrast features, corners etc) and measure the difference in angle from the two cameras. This is relatively simple and easy to understand!
For tasks that humans can do that involve depth perception of things more than ~40m away - flying a plane, for example, where most things are more than 40m away if you're doing it right! - nice simple stereo vision can't get the job done, because humans are actually using their other tricks.
Of course, despite this limitation stereo vision comes up a lot in nature - it's still a beneficial adaption, because most things in nature that will kill you do so from less than 10m away :)
It's actually pretty rare for non-predatory animals to have good stereo vision. Most of them are optimized for a wide field of view instead, evolving eyes placed on either side of their head. Think rabbits, parrots, bison, trout, iguanas, etc.
https://en.wikipedia.org/wiki/Binocular_vision
https://www.quora.com/Why-have-most-animals-evolved-to-see-o...
Edit: IMO binocular vision is probably more to do with redundancy than depth perception. If you damage or lose an eye, you can still operate at near full capacity. Losing vision 'in the wild' is a death sentence.
My experience is that waiting for cleaner data is often like waiting for Godot and will often be a project killer (sometimes justifiably). This is a key issue at the moment in advanced ML: clean large training sets ideal for supervised training are elusive and the companies making real-world advances are pretty much all using available data (and semi-supervised techniques) rather than expensive made up training sets.
You should talk to us at Leaflabs. Commercializing research-level tech in embedded electronics is what we do.
Maybe for regular "cameras", but parallax still gives the most accurate results for 3D reconstruction from satellite images, where it is a very lively area of research. The resolution, and surface coverage, of radar satellites is far worse than what you can obtain by stereo matching optical images taken by the powerful telescopes on satellites. I guess in other fields like microscopy it makes a lot of sense also. Not all imaging happens indoor on commodity cameras!
A lot of problems are not tackled on a fundamental level. Occlusion, context, proprioception, prediction, timing, attention, saliency, etc.
A simple rat has more intelligence than whatever is behind a dashcam, security cam or webcam.
Yes, a quality TOF system would be great. However good luck convincing consumers to adopt hardware with lidar on it. The Tango is having enough trouble on it's own and it does pretty well for consumer systems with IR.
Besides that you can't do FTDT with laser systems AFAIK. You need something to capture unseen places, such as ultrasonics/HF - which I guess you could argue fall into TOF but I haven't seen that work done.
In the end my money (literally!) is on the opposite if your approach, namely building better RGB systems because there are already a trillion cameras deployed that we can extract from.
First project Tango smartphone will be out soon.
http://shop.lenovo.com/us/en/smartphones/phab-series/phab2-p...
I think it first went on sale Nov. 1 but just started shipping initial batches recently.
(None of which takes from your point.)
They can have problems operating outside because it is hard to make a lightsource brighter than the sun.
I've been expecting good, cheap non-scanning laser distance imagers for a decade. In 2003, I went down to Advanced Scientific Concepts in Santa Barbara and saw the first prototype, as a collection of parts on an optical bench. Today ASC makes good units [2], but they cost about $100K. DoD and Space-X buy them. There's one on the Dragon spacecraft, for docking. That technology isn't inherently expensive, but requires custom semiconductors produced with non-standard processes such as InGaAs. Those cost too much in small volumes. There's been progress in coming up with designs that can be made in standard CMOS fabs.[3] When that hits production, laser rangefinders will cost like CMOS cameras.
[1] http://www.adept.net.au/cameras/Mesa/SR4000.shtml [2] http://www.advancedscientificconcepts.com/products/overview.... [3] https://books.google.com/books?id=Op6NCwAAQBAJ&lpg=PA64
Can anybody point me to some literature or reference materials about attempts to combine the inputs from multiple techniques simultaneously?
E.g. a device with stereo conventional cameras and infrared cameras & emitters which compares the resulting model from each input source/technique and actively re-adjusts final depth estimate?
Is "sensor fusion" the right jargon to use in this context?
Or, even crazier, a control system which actively jitters the camera's pose to gain more information for points in the depth map with lower confidence scores / conflicting estimates?
But maybe such a setup is overly complex and yields minimal gains in mixed indoor & outdoor scenarios?
One neat thing though you might want to look at: if all you have is structured light (ie Kinect v1) you can simply attach a vibrating motor to each emitter/receiver to avoid a lot of interference per [0]
[0] https://wwwx.cs.unc.edu/~maimone/media/kinect_VR_2012.pdf
The giant piles of dirty data are that way because for thirty years no one has considered them worth cleaning up. How will they create such astounding amounts of unexpected value?
It was left that way because we didn't have the tools to process it. Imagine the amount of unprocessed video data that we can now annotate pretty accurately. What's the value of that data now?
This said I still believe the article is mistaken in its evaluation of potential impact (and its fuzzy metaphore of pipes). Unstructured or semi-structured or dirty data is much more prevalent than cleaned structured data on which you can do simple regression to get insight.
Ultimately the class of problems solved by more advanced AI will be incommensurably bigger than the class of problems solved by simple machine learning. I could make a big laundry list but just start thinking of anything that involves images, sound, or text (ie most form of human communication).