Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
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Re: Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
#2Impressive demo.
Any FSD startup that put their money on LiDAR is even more screwed now.
Re: Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
#3Very interesting work! More details here: https://depth-anything.github.io/
It seems better overall and per parameter than current work, with relative and absolute measurement.
Is there any research people are aware of that provides sub-mm level models? For 3D modeling purposes? Or is "classic" photogrammetry still the best option there?
Re: Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
#4Impressive demo. Any FSD startup that put their money on LiDAR is even more screwed now.
Disagree there. Humans have massive compute, dual optics, and amazing filters.
Computer vision has 1-2 of those three, and I don't think we are near an AGI for self driving yet. Driving is IMO, an AGI level task.
Does you dataset have a crocodile in it? Does you monocular depth model get fooled by a billboard that's just a photo?
Re: Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
#5Very cool to see TikTok sharing its research.
Re: Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
#6Impressive demo. Any FSD startup that put their money on LiDAR is even more screwed now.
That's only a "happy path" attitude.
How well would a moncular path with headlights moving toward it at night operate? How about in rain, snow, or fog?
I'm not saying LiDAR is the only way, but I don't see a reason to use this as a solution.
I'm not saying this isn't valuable. I used to work in 3D/metaverse space, and having depth from a single photo, and being able to recreate a 3D scene from that is very valuable, and is the future.
Re: Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
#7[deleted]
Re: Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
#8Can someone explain the meaning of labelled vs unlabelled in this context? What kind of information would the labels carry?
Did they have depth maps for all 62 million images or not?
Re: Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
#9Impressive demo. Any FSD startup that put their money on LiDAR is even more screwed now.
Disagree there. Humans have massive compute, dual optics, and amazing filters. Computer vision has 1-2 of those three, and I don't think we are near an AGI for self driving yet. Driving is IMO, an AGI level task. Does you dataset have a crocodile in it? Does you monocular depth model get fooled by a billboard that's just a photo?
>Does you monocular depth model get fooled by a billboard that's just a photo?
This is actually a pretty clever example, I tried a few billboards on the demo online and, as these models are regressive so they output the mean of the possible outputs, sometimes the model is perplexed and doesn't seem to know if to output something completely flat or that actually has a depth, and by being perplexed it outputs something in between.
Re: Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
#10In grad school I was using stereo video cameras to measure fish. I wonder if a model like this could do it accurately from frame grabs from a single feed now. And of course an AI to identify fish, even if was just which sections of video had/did not have fish, not even doing the species level ID, would have saved a ton of time.
We had a whole workshop on various monitoring technologies and the take home from the various video tools is that having highly trained grad students and/or techs watch and analyze the video is extremely slow and expensive.
I haven't worked with video in a while now, but I wonder if any labs are doing more automated identification these days. It feels like the kind of problem that is probably completely solvable if the right tech gets applied.