DepthFM: Fast Monocular Depth Estimation with Flow Matching

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

📻 Overview

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.

🛠️ Setup

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-fm

Then 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.txt

or if you prefer to use conda create the conda environment via

conda env create -f environment.yml

Now you should be able to listen to DepthFM! 📻 🎶

🚀 Usage

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.ckpt

The 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.

📈 Results

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.

Trend

🎓 Citation

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}
}