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I'm about to start developing a neural net here with Tensorflow, but before I get into it too deep, I was hoping I could get some feedback on exactly what type of neural net I will need for this (If a net is the right way to go about this at all)

I need the NN to input an image, and output another image. This will be used for path-mapping on a robot I'm working on. The input image will be a disparity map, and the output will be a "driveable map" (an image that displays what in the scene can be driven on, and what can't)

I have built a dataset using Unity 3d. Here is an example from the set:

disparity map

Disparity Map

driveable map:

Driveable map

As you can probably see, white represents the area where my robot can drive and black is where it can't. I will need the NN to take a disparity map, and give me back a "driveable map". Can this be done? Thanks!

Zock77
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1 Answers1

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Sorry I'm not an expert. Since there hasn't been a response on this and if you are still looking, the vocabulary I would use to describe this type of problem is disparity networks and segmentation. Your best bet may be a specific type of disparity network: U-net

Sam
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    Thanks for the response! After looking into it a bit more, the type of network that I needed for this was a "Segmentation Net" as you mentioned. This inputs an image, and outputs an image. (Exactly what I was looking for) – Zock77 Apr 16 '18 at 20:04