GBNet : Gradient Boosting Network for Monocular Depth Estimation

This is the reference PyTorch implementation for training and testing depth estimation models, and it based on the PackNet code.

Install

We recommend using docker. To setup your environment, type in a terminal:

git clone https://github.com/sejong-rcv/GBNet
cd GBNet
# if you want to use docker (recommended)
make docker-build

if you want to run directly inside our container, you can do it in one step:

# single GPU
make docker-run COMMAND="some-command"
# multi-GPU
make docker-run-mpi COMMAND="some-command"

If you want to run any of the commands in container, you can do in one step:

make docker-start-interactive

If you want to use features related Weights & Biases (WANDB) (for experiment management/visualization), then you should create associated accounts and configure your shell with the following environment variables:

export WANDB_ENTITY="something"
export WANDB_API_KEY="something"

To enable WANDB logging, you can then set the corresponding configuration parameters in configs/.yaml (cf. configs/default_config.py for defaults and docs):

wandb:
    dry_run: True                                 # Wandb dry-run (not logging)
    name: ''                                      # Wandb run name
    project: os.environ.get("WANDB_PROJECT", "")  # Wandb project
    entity: os.environ.get("WANDB_ENTITY", "")    # Wandb entity
    tags: []                                      # Wandb tags
    dir: ''                                       # Wandb save folder

Datasets

Datasets are assumed to be downloaded in /data/datasets/ (can be a symbolic link).

Dense Depth for Autonomous Driving (DDAD)

curl -s https://tri-ml-public.s3.amazonaws.com/github/DDAD/datasets/DDAD.tar | tar -xv -C /data/datasets/

Training

• To initialize the packnet, download pre-trained weight in github

python3 scripts/train.py configs/train_ddad.yaml

Evaluation

python3 scripts/eval.py --checkpoint <checkpoint.ckpt> --config configs/eval_ddad.yaml

You can also directly run inference on a single image or folder:

python3 scripts/infer.py --checkpoint <checkpoint.ckpt> --input <image or folder> --output <image or folder> [--image_shape <input shape (h,w)>]

Models

DDAD

Model	Abs.Rel.	Sqr.Rel	RMSE	RMSElog	d < 1.25
ResNet18, Self-Supervised, 384x640, ImageNet → DDAD (D)	0.227	11.293	17.368	0.303	0.758
PackNet, Self-Supervised, 384x640, DDAD (D)	0.173	7.164	14.363	0.249	0.835
GBNet, Self-Supervised, 384x640, DDAD (D)	0.148	3.329	14.471	0.244	0.818
GBNet, Semi-Supervised, 384x640, DDAD (D)	0.124	2.476	13.276	0.220	0.846

References

GBNet is based on PackNet code.

GBNet : Gradient Boosting Network for Monocular Depth Estimation (ICCAS)
Daechan Han and Yukyung Choi

@inproceedings{GBNet,
  author = {Daechan Han and Yukyung Choi},
  title = {GBNet : Gradient Boosting Network for Monocular Depth Estimation},
  booktitle = {International Conference of Contral, Automation and Systems(ICCAS)},
  year = {2021},
}

3D Packing for Self-Supervised Monocular Depth Estimation
Vitor Guizilini, Rares Ambrus, Sudeep Pillai, Allan Raventos and Adrien Gaidon

@inproceedings{packnet,
  author = {Vitor Guizilini and Rares Ambrus and Sudeep Pillai and Allan Raventos and Adrien Gaidon},
  title = {3D Packing for Self-Supervised Monocular Depth Estimation},
  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  primaryClass = {cs.CV}
  year = {2020},
}