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GLPDepth PyTorch Implementation: Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth

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Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth [Paper]

PWC PWC

Downloads

  • [Downloads] Trained ckpt files for NYU Depth V2 and KITTI
  • [Downloads] Predicted depth maps png files for NYU Depth V2 and KITTI Eigen split test set

Google Colab

Open In Colab

Thanks for the great Colab demo from NielsRogge

Requirements

Tested on

python==3.7.7
torch==1.6.0
h5py==3.6.0
scipy==1.7.3
opencv-python==4.5.5
mmcv==1.4.3
timm=0.5.4
albumentations=1.1.0
tensorboardX==2.4.1
gdown==4.2.1

You can install above package with

$ pip install -r requirements.txt

Or you can pull docker image with

$ docker pull doyeon0113/glpdepth

Inference and Evaluate

Dataset

NYU Depth V2
$ cd ./datasets
$ wget http://horatio.cs.nyu.edu/mit/silberman/nyu_depth_v2/nyu_depth_v2_labeled.mat
$ python ../code/utils/extract_official_train_test_set_from_mat.py nyu_depth_v2_labeled.mat splits.mat ./nyu_depth_v2/official_splits/
KITTI

Download annotated depth maps data set (14GB) from [link] into ./datasets/kitti/data_depth_annotated

$ cd ./datasets/kitti/data_depth_annotated/
$ unzip data_depth_annotated.zip

With above two instrtuctions, you can perform eval_with_pngs.py/test.py for NYU Depth V2 and eval_with_pngs for KITTI.

To fully perform experiments, please follow [BTS] repository to obtain full dataset for NYU Depth V2 and KITTI datasets.

Your dataset directory should be

root
- nyu_depth_v2
  - bathroom_0001
  - bathroom_0002
  - ...
  - official_splits
- kitti
  - data_depth_annotated
  - raw_data
  - val_selection_cropped

Evaluation

  • Evaluate with png images

    for NYU Depth V2

    $ python ./code/eval_with_pngs.py --dataset nyudepthv2 --pred_path ./best_nyu_preds/ --gt_path ./datasets/nyu_depth_v2/ --max_depth_eval 10.0 
    

    for KITTI

    $ python ./code/eval_with_pngs.py --dataset kitti --split eigen_benchmark --pred_path ./best_kitti_preds/ --gt_path ./datasets/kitti/ --max_depth_eval 80.0 --garg_crop
    
  • Evaluate with model (NYU Depth V2)

    Result images will be saved in ./args.result_dir/args.exp_name (default: ./results/test)

    • To evaluate only

      $ python ./code/test.py --dataset nyudepthv2 --data_path ./datasets/ --ckpt_dir <path_for_ckpt> --do_evaluate  --max_depth 10.0 --max_depth_eval 10.0
      
    • To save pngs for eval_with_pngs

      $ python ./code/test.py --dataset nyudepthv2 --data_path ./datasets/ --ckpt_dir <path_for_ckpt> --save_eval_pngs  --max_depth 10.0 --max_depth_eval 10.0
      
    • To save visualized depth maps

      $ python ./code/test.py --dataset nyudepthv2 --data_path ./datasets/ --ckpt_dir <path_for_ckpt> --save_visualize  --max_depth 10.0 --max_depth_eval 10.0
      

    In case of kitti, modify arguments to --dataset kitti --max_depth 80.0 --max_depth_eval 80.0 and add --kitti_crop [garg_crop or eigen_crop]

Inference

  • Inference with image directory
    $ python ./code/test.py --dataset imagepath --data_path <dir_to_imgs> --save_visualize
    

Train

for NYU Depth V2

$ python ./code/train.py --dataset nyudepthv2 --data_path ./datasets/ --max_depth 10.0 --max_depth_eval 10.0  

for KITTI

$ python ./code/train.py --dataset kitti --data_path ./datasets/ --max_depth 80.0 --max_depth_eval 80.0  --garg_crop

To-Do

  • Add inference
  • Add training codes
  • Add dockerHub link
  • Add colab

License

For non-commercial purpose only (research, evaluation etc).

Citation

@article{kim2022global,
  title={Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth},
  author={Kim, Doyeon and Ga, Woonghyun and Ahn, Pyungwhan and Joo, Donggyu and Chun, Sehwan and Kim, Junmo},
  journal={arXiv preprint arXiv:2201.07436},
  year={2022}
}

References

[1] From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation. [code]

[2] SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers. [code]

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GLPDepth PyTorch Implementation: Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth

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