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Moving Camera, Moving People: A Deep Learning Approach to Depth Prediction

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Re: Moving Camera, Moving People: A Deep Learning Approach to Depth Prediction

#4
I can’t wait until techniques like this find their way in to open source photogrammetry pipelines. I came up with a way of training neural nets for robotics using a monocular camera, photogrammetry, and a simulation environment with the captured 3D scene, but the photogrammetry was error prone and computationally intensive even on a beefy cloud server.

I’d love for OpenSFM or OpenMVS (check github) to get this kind of software.

Also would love to see an implementation of this on github, but hopefully that will follow in time.

Re: Moving Camera, Moving People: A Deep Learning Approach to Depth Prediction

#6
post #2

Can other companies use YouTube database for free say for research in Computer vision?

I think it's a gray area, but researchers often just do it. Better to ask for forgiveness than permission I guess. You could never collect datasets like ImageNet if you had to obtain individual permissions.

Re: Moving Camera, Moving People: A Deep Learning Approach to Depth Prediction

#9
post #2

Can other companies use YouTube database for free say for research in Computer vision?

Likely to fall under fair use, both for the research aspect and given the very low impact on factors 3&4 of 17USC107 for videos used to train neural nets.

Re: Moving Camera, Moving People: A Deep Learning Approach to Depth Prediction

#10
This is a great hack, but I'd love to see more detail on how they did pose initialization to approximate ground truth on depth/pose from the Mannequin set. The paper says they are using ORB-SLAM2, but AFAIK ORB still needs a height label.

Maybe it's the case that this system doesn't actually return an X,Y,Z camera pose, but rather just a pixel specific depth, and not a recovered pose for new inputs.

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