1. As others have pointed out, the ImageNet experiment is presented as evidence that (as you’d expect) adding coordinate channels doesn’t affect performance on image classification tasks. That’s a good “sanity check” experiment to have done.
2. The paper proposes a simple idea, and it may not have been necessary to give it a whole new name (CoordConv). But if you’d asked me if I thought that adding coordinate data to the input would have led to significantly better object detection, I wouldn’t have known the answer, so the results of their experiments—that it does help on tasks like object detection—is not trivial. Not only that—a lot of people have tried to do object detection, and yet nobody has reported adding input channels for storing coordinates before. A lot of ideas seem simple after someone thinks of them.
3. Toy examples are useful for testing intuition (and building intuition about why this trick may be helpful and for what kinds of tasks). The fact that we can easily imagine what sorts of weights we’d expect the network to learn is one of the things that makes it a good toy example. (Of course, the paper wouldn’t be worth publishing if it only had the toy example.)