It seems that we still don't have a real breakthrough training a machine learning algorithm on synthetic camera images alone and using it in real applications.

It seems that we still don't have a real breakthrough training a machine learning algorithm on synthetic camera images alone and using it in real applications. This is already done successfully for depth images by Microsoft for the Kinect [1], but I haven't seen something like that for normal images. The GTA V dataset is close, but not the real thing... [2] It is difficult to create high-quality photo realistic images at big scale with enough variance. It is interesting to see, if one can train a network that transfers images (both synthetic and real) to some intermediate representation and then train some detector/classifier/semantic segmentation on it... I had a lot of fun playing around with a open-source game engine called VDrift to generate ground truth for optical flow, depth and semantic segmentation. I think the video with the ground truth is nice [3], but the graphics of the game weren't that good. All the code is open-sourced on Github if somebody feels like playing around... [4] [1] https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/main-39.pdf https://www.microsoft.com/en-us/research/wp-content/uploads/... [2] http://vladlen.info/papers/playing-for-data.pdf http://vladlen.info/papers/playing-for-data.pdf [3] https://vimeo.com/haltakov/synthetic-dataset https://vimeo.com/haltakov/synthetic-dataset [4] https://github.com/haltakov/synthetic-dataset https://github.com/haltakov/synthetic-dataset