carbocation t1_ixyz3g4 wrote
Seems like binocular depth estimation should be possible with a binocular device.
naccib t1_iy1009o wrote
Monocular depth estimation is very valuable for creating AR experiences in general-use devices such as smartphones. This is, in my opinion, the greatest value for such depth estimation algorithms.
carbocation t1_iy12bhd wrote
I agree with you about the value and use-cases for monocular depth estimation. I was just making the point that, in principle, a binocular device could attempt binocular depth estimation. Or perhaps they tried it internally and it was not sufficiently better to be worth the expense.
naccib t1_iy3jayh wrote
Oh, binocular depth estimation is definitely a less technically challenging approach. I think the reasons they are pursuing monocular are due to what the other commenter said about cost and stuff.
pm_me_your_pay_slips t1_ixz544s wrote
One camera is cheaper than two, though. Cheaper in every sense (compute, memory, network bandwidth, energy consumption, parts cost, etc).
mg31415 t1_iy2pg4i wrote
How is one camera is cheaper computationally? If it was stereo they wouldn't need a NN
pm_me_your_pay_slips t1_iy2twej wrote
You need to do feature computation and find correspondences. If you’re using a learned feature extractor, that will be twice as expensive as the monocular model. But let’s say you’re using a classical feature extractor. You still need to do feature matching. For dense depth maps, both of these stages can be as expensive, if not more, than a single forward pass through a highly optimized mobile NN architecture.
soulslicer0 t1_iy2fd1x wrote
Could be doing depth estimation by fusing two monocular nets like mvsnet
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