It's actually pretty simple/clever. Constructing labeled imagery costs a lot of time and effort. I assume the current approach is to have a bunch of humans (und

It's actually pretty simple/clever. Constructing labeled imagery costs a lot of time and effort. I assume the current approach is to have a bunch of humans (undergrads, most likely) sit through every image and label (color) them: these pixels are trees, these are cars. Probably it's relatively error prone as well. The authors propose to just use <some open world game> to take a huge bunch of images. Since we're talking about a game, the computer has a perfect internal representation of entities and hence things that can be considered cars, trees, streets, etc. We can thus not only obtain an image per frame that looks close to the real-world, but immediately also one that is labelled. Why is this helpful? To train computer vision models such as the ones used in self-driving cars. Of course, the assumption here is that the imagery obtained from a game is close enough to the real world, so that a trained model would continue to work in the real world. I haven't read the paper in full, but the authors experiments show that this is the case. They still use some original imagery though, so perhaps it's not possible to use game-imagery alone. I also don't think an experiment was performed to see if this method would still hold up when using games having older, worse looking engines (it would be interesting to see whether deep models could still generalize towards the real world from this). Finally, the authors spend a lot of hacky efforts in forcing the game to outputting labelled images. As others have suggested here, they probably would have been better off contacting some mod authors (who could whip this up in a day, probably) or even the game developer itself (though I don't think Rockstar would be particularly interested to collaborate on this).