disparity-map | Disparity maps using various algorithms | Computer Vision library

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

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!

I was doing stereo camera calibration using Python 2.7 and OpenCV 3.3. The code I used is (I got it from Stereo Calibration Opencv Python and Disparity Map):

I've been experimenting with a homemade stereo camera, using a pair of webcams. I've taken several pictures and used OpenCV for calibration.

Rectified sample images:

The calibration chessboards seem to be lined up horizontally.

But when I generate a disparity map based on these instructions, the result seems meaningless.

My disparity-map code is fairly trivial: