Yes, in general if you can capture multiple views you can make up for many dropouts and artifacts. The more you can move, the more likely that you'll get a comp

Yes, in general if you can capture multiple views you can make up for many dropouts and artifacts. The more you can move, the more likely that you'll get a complete reconstruction. Single-view artifacts are more of a problem when reaching into confined areas or during visual servoing where you don't necessarily have the freedom to move around to get a better view. The results for the Sintel benchmark do look interesting. Do you have a report or some sort of overview of your approach? It would be nice to see a similar benchmark on real recorded scenes, especially if that provided a way to compare learned matching algorithms with available sensor hardware. I think there's reasonable promise in learned stereo matching, especially if we include more information than just visible spectra. Human eyes are so much more than two RGB imagers, so we shouldn't limit our robots to that either. Monocular depth, I agree, seems to be mostly a memorization problem. In cases where you can effectively memorize everything, it will work quite well and the savings in hardware complexity (and physical sensor size) will be well worth the training complexity. I actually think it has much more promise as a backup depth perception method on cars, since objects in a driving context are mostly consistent in size. I have doubts about how useful it will be in manipulation tasks where you may encounter similar objects at a range of sizes. I'm part of a research group at TRI working on home manipulation tasks although I have something of a hobby interest in outdoor robotics as well - in general I'd say the depth sensors for outdoor tasks are often better, but much more expensive and out of reach for hobbyist users.