Intel / zoedepth-nyu-kitti

ZoeDepth (fine-tuned on NYU and KITTI)

ZoeDepth model fine-tuned on the NYU and KITTI datasets. It was introduced in the paper ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth by Shariq et al. and first released in this repository .

ZoeDepth extends the DPT framework for metric (also called absolute) depth estimation, obtaining state-of-the-art results.

Disclaimer: The team releasing ZoeDepth did not write a model card for this model so this model card has been written by the Hugging Face team.

Model description

ZoeDepth adapts DPT , a model for relative depth estimation, for so-called metric (also called absolute) depth estimation.

This means that the model is able to estimate depth in actual metric values.

ZoeDepth architecture. Taken from the original paper.

Intended uses & limitations

You can use the raw model for tasks like zero-shot monocular depth estimation. See the model hub to look for other versions on a task that interests you.

How to use

The easiest is to leverage the pipeline API which abstracts away the complexity for the user:

from transformers import pipeline
from PIL import Image
import requests

# load pipe
depth_estimator = pipeline(task="depth-estimation", model="Intel/zoedepth-nyu-kitti")

# load image
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)

# inference
outputs = depth_estimator(image)
depth = outputs.depth

For more code examples, we refer to the documentation .

BibTeX entry and citation info

@misc{bhat2023zoedepth,
      title={ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth}, 
      author={Shariq Farooq Bhat and Reiner Birkl and Diana Wofk and Peter Wonka and Matthias Müller},
      year={2023},
      eprint={2302.12288},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}