Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth [Paper]
- [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
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
$ 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/
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
-
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 10.0for 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 80.0and add--kitti_crop [garg_crop or eigen_crop] -
- Inference with image directory
$ python ./code/test.py --dataset imagepath --data_path <dir_to_imgs> --save_visualize
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
- Add inference
- Add training codes
- Add dockerHub link
- Add colab
For non-commercial purpose only (research, evaluation etc).
@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}
}
[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]
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent