Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data (opens in new tab)

Nothing would benefit the scientific enterprise more than explicitly publishing papers about failed experiments.

How much public money, time, and careers have been wasted chasing something that is already known not to work?

Definitely not with this model, because it’s impossible to tell based on the distance alone. Is the fish 34cm away and 34cm long or 30cm away and 30cm long? The fish is floating in a transparent medium, so reference points aren’t even useful as calibration.

Are the fish always the same color/is their color distinct from the background.

I work at an industrial plant we have been able to measure a lot of things simply by analyzing the pixels in the video. For example one application we have a camera pointed down at a conveyor belt. The conveyor belt is one color and objects on the belt are a distinct different color.

- we just count how many pixels in a given frame are a specific color/brightness. Then you can easily work out how much of the conveyor belt has material on it in any given frame.

So if you are tying to work out what section of a video has fish in it you could count how many pixels are a different color to the normal background color.

You can definitely train a model to identify fish, to be honest you don't really have to train a whole model, there are tons of models trained on millions of images, you can just extract the embeddings from those models and train a single matrix to project them to the different classes and it will work very well.

Very likely. Tbh, I think there are a lot of domain tasks where if you added a machine learning expert to the team that success and progress would be a lot higher. But to be fair, there are are a lot of people that can do ML but not a lot of people that have a deep understanding. The difference matters for real world tasks when the difference between dataset performance and generalization performance matter. And it's all too common that works that are SOTA are more difficult to generalize, but this is high variance.

They explain in the paper that they used 1.5 million images with known depth maps (labels) to train a teacher model, and then used the teacher model to create pseudolabels (inferred depth maps) for the full dataset. Then they trained a student model to recover those pseudolabels from distorted versions of the original images.

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?

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.