> The ‘magic’ of the monodepth DNNs is exactly in their ability to learn what scale objects and scenery tends to be in the training data, rather than having to

> The ‘magic’ of the monodepth DNNs is exactly in their ability to learn what scale objects and scenery tends to be in the training data, rather than having to be modeled one object at a time. Yes, and for controlled industrial environments where the domain can be effectively captured in the training dataset, this is plausible. Although a fair number of industrial applications do contain very similar objects at multiple scales, and I don't think much of the existing monodepth work has been meaningfully trained or tested on these cases. > The human visual system has the same limitation Only when looking at picture, not when looking directly at the scene even if you only have one eye. Human vision (and that of many animals, even very small ones like jumping spiders) uses information about the focusing of the eye itself to effectively recover real depth (this mechanism, like structure-from-focus on cameras, is obviously more effective at close distances with shallow depth of field and less effective as the distance and depth of field increase). And, of course, the scanning + focusing behavior of the eye is very different than a perfectly-exposed global-shutter planar imager with deep depth-of-field (which is what the "ideal" car camera would be). > A self driving car camera will never in practice find itself peering into a little box of toy cars. Ignoring the case of someone deliberately trying to confuse the car, it will find itself looking at a large variety of children, of different ages, body shapes and sizes, and clothing - and a scale error there can easily have fatal results. Lest this be considered far-fetched, one particular application of monodepth that has been considered by automakers is car backup/360-view cameras, an application area that exists largely because drivers back over small children with alarming frequency.