Except that DL can't provide the same things as a lot of CV research. They are two separate areas of research for different problems.

DL doesn't provide solutions for challenges in robotics or augmented reality that CV is very good at. For example it can't place the camera at a specific position in the world using the image. DL can tell us what's in the image which CV can't. But CV can place the locations of those objects relative to the viewer.

> For example it can't place the camera at a specific position in the world using the image.

I might be mistaken but isn't that exactly what this author's research was about? Camera pose estimation via Deep Learning ("PoseNet").

Yes, but if you read the paper you'll see that he's talking about tightening the gap between the Deep Learning approaches and the current features + geometry state of the art, which is an order of magnitude more precise. DL approaches are however quite faster.

Camera Pose estimation is nearly solved with DL. In fact we're rolling out an application through a major retailer this summer that does exactly that.

It's not a fundamental limitation. People just haven't gotten to that yet, since there's so much low-hanging fruit with DL.