Monocular Depth Estimation for NYU2

Pytorch re-implementation of the below paper.

• Python 3.6.8
• PyTorch 1.6.0

Please see requirements.txt for the other libraries' versions.

Paper

Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps with Accurate Object Boundaries

Junjie Hu, Mete Ozay, Yan Zhang, Takayuki Okatani

WACV2019

[arXiv] [Original repo]

Results in the paper:

*The figures are from the paper.

Results in this repository

We use ResNet-50 for the main results. Please see the config file for the other parameters.

Training loss

Quantitative results

MAE	MSE	RMSE	ABS_REL	LG10	DELTA1	DELTA2	DELTA3
0.3388	0.3150	0.5613	0.1283	0.0548	0.8407	0.9673	0.9907

Qualitative results

Others

• The results about other metrics and the ablation study results are here.
• 2021.5.1 For the request in #1, we uploaded a newly trained model here. (Note that the scores are different from the ones above. See #1 for the details about this model.)

Preparation

Dataset: NYU v2

sh scripts/prepare_nyu2.sh

This script uses the downloading link in J. Hu's repository.

Installation

docker-compose build
docker-compose run dev

• docker-compose 1.28.3
• Docker 20.10.2

ref. [Enabling GPU access with Compose]

Installation w/ nvidia-docker

nvidia-docker build -t {IMAGE_NAME} ./docker
nvidia-docker run -it -v `pwd`:/work -v $HOME/data/nyu2/data:/work/data --name {CONTAINER_NAME} {IMAGE_NAME}

Please mount your working directory on /work, and the dataset path on /work/data.

Installation w/o Docker

pip install -r ./docker/requirements.txt

Note that the libraries will be installed into your environment.

Please place the dataset in ./data in your working directory, or change the dataset path in your config file.

Run

Train

python tools/train.py

Option	Description
--config [config path]	Optional config file path. The configs/default.yaml is loaded by default. The specified file overwrites the default configs.
--out-dir [outdir path]	Output directory path. (default: results)
--resume [ckpt path]	Resuming checkpoint file path.

If you want to override the config with command line args, put them at the end in the form of dotlist.

python tools/train.py --config [config path] SOLVER.NUM_WORKERS=8 SOLVER.EPOCH=5

Test

python tools/test.py [ckpt path]

Option	Description
--config [config path]	The optional config file path.used when training.
--show-dir [outdir path]	Path to save predict visualization. Please specify if you want to save.

Other tools

Please see tools/README.md.

Credit

@inproceedings{Hu2019RevisitingSI,
  title={Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps With Accurate Object Boundaries},
  author={Junjie Hu and Mete Ozay and Yan Zhang and Takayuki Okatani},
  journal={2019 IEEE Winter Conference on Applications of Computer Vision (WACV)},
  year={2019}
}