Ming Gui* · Johannes S. Fischer* · Ulrich Prestel · Pingchuan Ma
Dmytro Kotovenko · Olga Grebenkova · Stefan A. Baumann · Vincent Tao Hu · Björn Ommer
CompVis Group @ LMU Munich
* equal contribution
We present DepthFM, a state-of-the-art, versatile, and fast monocular depth estimation model. DepthFM is efficient and can synthesize realistic depth maps within a single inference step. Beyond conventional depth estimation tasks, DepthFM also demonstrates state-of-the-art capabilities in downstream tasks such as depth inpainting and depth conditional synthesis.
With our work we demonstrate the successful transfer of strong image priors from a foundation image synthesis diffusion model (Stable Diffusion v2-1) to a flow matching model. Instead of starting from noise, we directly map from input image to depth map.
This setup was tested with Ubuntu 22.04.4 LTS, CUDA Version: 12.2, and Python 3.11.5.
First, clone the github repo...
git clone git@github.com:CompVis/depth-fm.git
cd depth-fmThen download the weights via
wget https://ommer-lab.com/files/depthfm/depthfm-v1.ckpt -P checkpoints/Now you have either the option to setup a virtual environment and install all required packages with pip
pip install -r requirements.txtor if you prefer to use conda create the conda environment via
conda env create -f environment.ymlNow you should be able to listen to DepthFM! 📻 🎶
You can either use the notebook inference.ipynb or just run the python script inference.py as follows
python inference.py \
--num_steps 2 \
--ensemble_size 4 \
--img assets/dog.png \
--ckpt checkpoints/depthfm-v1.ckptThe argument --num_steps allows you to set the number of function evaluations. We find that our model already gives very good results with as few as one or two steps. Ensembling also improves performance, so you can set it via the --ensemble_size argument. Currently, the inference code only supports a batch size of one for ensembling.
Our quantitative analysis shows that despite being substantially more efficient, our DepthFM outperforms the current state-of-the-art generative depth estimator Marigold zero-shot on a range of benchmark datasets. Below you can find a quantitative comparison of DepthFM against other affine-invariant depth estimators on several benchmarks.
Please cite our paper:
@misc{gui2024depthfm,
title={DepthFM: Fast Monocular Depth Estimation with Flow Matching},
author={Ming Gui, Johannes S. Fischer, Ulrich Prestel, Pingchuan Ma, Dmytro Kotovenko, Olga Grebenkova, Stefan Andreas Baumann, Vincent Tao Hu, Björn Ommer},
year={2024},
eprint={2403.13788},
archivePrefix={arXiv},
primaryClass={cs.CV}
}