README.md

Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer

This repository contains code to compute depth from a single image. It accompanies our paper:

Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer
René Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, Vladlen Koltun

The pre-trained model corresponds to DS 4 with multi-objective optimization enabled.

Changelog

• [Jul 2020] Added TensorFlow and ONNX code. Added online demo.
• [Dec 2019] Released new version of MiDaS - the new model is significantly more accurate and robust
• [Jul 2019] Initial release of MiDaS (Link)

Online demo

An online demo of the model is available: http://35.202.76.57/

Please be patient. Inference might take up to 30 seconds due to technical restrictions.

Setup

1. Download the model weights model-f45da743.pt and place the file in the root folder.

2. Set up dependencies:

   conda install pytorch torchvision opencv

   The code was tested with Python 3.7, PyTorch 1.2.0, and OpenCV 3.4.2.

Usage

1. Place one or more input images in the folder input.

2. Run the model:

   python run.py

3. The resulting inverse depth maps are written to the output folder.

via Docker

1. Make sure you have installed Docker and the NVIDIA Docker runtime.

2. Build the Docker image:

   docker build -t midas .

3. Run inference:

   docker run --rm --gpus all -v $PWD/input:/opt/MiDaS/input -v $PWD/output:/opt/MiDaS/output midas

   This command passes through all of your NVIDIA GPUs to the container, mounts the input and output directories and then runs the inference.

via TensorFlow or ONNX

See README in the tf subdirectory.

Citation

Please cite our paper if you use this code or any of the models:

@article{Ranftl2019,
	author    = {Ren\'{e} Ranftl and Katrin Lasinger and David Hafner and Konrad Schindler and Vladlen Koltun},
	title     = {Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer},
	journal   = {arXiv:1907.01341},
	year      = {2019},
}

License

MIT License