Depth-Anything VS ml-engineering

Depth estimation improved a lot as well e.g. with Depth-Anything [0]. But those are mostly relative depth instead of metric. Also when even converted to metric they still seems have a lot of pointclouds at the edges that have to be pruned - visible in this blog [1]. Looks like those models trained on Lidar or Stereo depthmaps that has this limitations. I think we don't have enough clean training data for 3d unless we maybe train on synthetic data (then we can have plenty, generate realistic scene in Unreal Engine 5 and train on rendered 2d frames)

[0] https://github.com/LiheYoung/Depth-Anything

[1] https://medium.com/@patriciogv/the-state-of-the-art-of-depth...

Very 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?

There is a lot of work to make the actual infrastructure and lower level management of lots and lots of GPUs/TPUs open as well - my team focuses on making the infrastructure bit at least a bit more approachable on GKE and Kubernetes.

https://github.com/GoogleCloudPlatform/ai-on-gke/tree/main

and

https://github.com/google/xpk (a bit more focused on HPC, but includes AI)

and

https://github.com/stas00/ml-engineering (not associated with GKE, but describes training with SLURM)

The actual training is still a bit of a small pool of very experienced people, but it's getting better. And every day serving models gets that much faster - you can often simply draft on Triton and TensorRT-LLM or vLLM and see significant wins month to month.

OK, the pdf is ready now: https://github.com/stas00/ml-engineering#pdf-version

this might be interesting: https://github.com/stas00/ml-engineering/blob/master/transformers/make-tiny-models.md