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You wouldn't use monodepth for self-driving, but I think it's useful for producing natural-looking image transformations; after all, you're working with the sam

You wouldn't use monodepth for self-driving, but I think it's useful for producing natural-looking image transformations; after all, you're working with the same image onscreen that a human eye sees (both the computer and the human eye looking at an onscreen photo have access to the same number of bits of information), so if you can get an accurate read on what a human would visually process the image depth to be, you can use that for reasonable-looking changes. I'm a little surprised there isn't more research on producing depth from short videos, though, since iPhones take Live Photos and presumably could use more of the natural movement and shakiness to interpret more of the true depth in the scene... Presumably much better than processing a single still.

cpgxiii 2y ago
> You wouldn't use monodepth for self-driving... If you look where a lot of the money has gone into monodepth, self-driving or driver assistance isn't too far away... Actually, I tend to think self-driving is one of the few places you can make a case for monodepth, as a backup for failures in the rest of your depth sensing suite. You wouldn't want to use it as a primary sensor, but if you're a vehicle driving on a highway and you take critical damage to some of your sensors you do still have to keep driving, if only long enough to get safely off the road, and having something that only needs a single camera is very valuable.
robotresearcher 2y ago
There’s a huge literature on ‘structure from motion’ which uses image sequences from a moving camera.
Someone 2y ago
>> The monodepth space is full of people insisting that their models can produce metric depth with no explanation other than "NN does magic" for why metric depth is possible from generic mono images. If you provide a single arbitrary image, you can't generate depth that is immune from scale error (e.g. produce accurate depth for a both an image of a real car and a scale model of the same car) > You wouldn't use monodepth for self-driving I don’t think you need accurate metric depth for self-driving. “seconds to impact” plus a reasonable estimate of one’s speed (one second from impact at 100km/hour is more dangerous than at 1km/hour both because braking distances grow faster than linear and because a hit at lower speed is less damaging) is way more useful than “meters away” for that.

cpgxiii 2y ago

> You wouldn't use monodepth for self-driving... If you look where a lot of the money has gone into monodepth, self-driving or driver assistance isn't too far away... Actually, I tend to think self-driving is one of the few places you can make a case for monodepth, as a backup for failures in the rest of your depth sensing suite. You wouldn't want to use it as a primary sensor, but if you're a vehicle driving on a highway and you take critical damage to some of your sensors you do still have to keep driving, if only long enough to get safely off the road, and having something that only needs a single camera is very valuable.

robotresearcher 2y ago

There’s a huge literature on ‘structure from motion’ which uses image sequences from a moving camera.

Someone 2y ago

>> The monodepth space is full of people insisting that their models can produce metric depth with no explanation other than "NN does magic" for why metric depth is possible from generic mono images. If you provide a single arbitrary image, you can't generate depth that is immune from scale error (e.g. produce accurate depth for a both an image of a real car and a scale model of the same car) > You wouldn't use monodepth for self-driving I don’t think you need accurate metric depth for self-driving. “seconds to impact” plus a reasonable estimate of one’s speed (one second from impact at 100km/hour is more dangerous than at 1km/hour both because braking distances grow faster than linear and because a hit at lower speed is less damaging) is way more useful than “meters away” for that.