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TiO-Depth_pytorch

This is the official repo for our work 'Two-in-One Depth: Bridging the Gap Between Monocular and Binocular Self-supervised Depth Estimation' (ICCV 2023). Paper
Citation information:

@article{zhou2023two,
  title={Two-in-One Depth: Bridging the Gap Between Monocular and Binocular Self-supervised Depth Estimation},
  author={Zhou, Zhengming and Dong, Qiulei},
  journal={arXiv preprint arXiv:2309.00933},
  year={2023}
}

Setup

We built and ran the repo with CUDA 11.0, Python 3.7.11, and Pytorch 1.7.0. For using this repo, we recommend creating a virtual environment by Anaconda. Please open a terminal in the root of the repo folder for running the following commands and scripts.

conda env create -f environment.yml
conda activate pytorch170cu11

Pre-trained models

• code for all the download links of Baidu is smde

Prediction

To predict depth maps for your images, please firstly download the pretrained model from the column named Model Name in the above table. After unzipping the downloaded model, you could predict the depth maps for your images by

python predict.py\
 --image_path <path to your image or folder name for your images>\
 --exp_opts <path to the method training option>\
 --model_path <path to the downloaded or trained model>

You also could set --input_size to decide the size that the images are reshaped before they are input to the model. If you want to predict on CPU, please set --cpu. The depth results <image name>_pred.npy and the visualization results <image name>_visual.png will be saved in the same folder as the input images.

Data preparation

Set data path

We give an example path_example.py for setting the path in the repository. Please create a python file named path_my.py and copy the code in path_example.py to the path_my.py. Then you can replace the used paths to your folder in the path_my.py. the folder for each dataset should be organized like:

<root of kitti>
|---2011_09_26
|   |---2011_09_26_drive_0001_sync
|   |   |---image_02
|   |   |---image_03
|   |   |---velodyne_points
|   |   |---...
|   |---2011_09_26_drive_0002_sync
|   |   |---image_02
|   |   |---image_03
|   |   |---velodyne_points
|   |   |---...
|   '''
|---2011_09_28
|   |--- ...
|---gt_depths_raw.npz (for raw Eigen test set)
|---gt_depths_improved.npz (for improved Eigen test set)

<root of kitti 2015>
|---training
|   |---image_2
|   |   |---000000_10.png
|   |   |---000000_11.png
|   |   |---000001_10.png
|   |   |---...
|   |---image_3
|   |   |---000000_10.png
|   |   |---000000_11.png
|   |   |---000001_10.png
|   |   |---...
|   |---disp_occ_0
|   |   |---000000_10.png
|   |   |---000000_11.png
|   |   |---000001_10.png
|   '''
|---testing
|   |--- ...

KITTI

For training the methods on the KITTI dataset (the Eigen split), you should download the entire KITTI dataset (about 175GB) by:

wget -i ./datasets/kitti_archives_to_download.txt -P <save path>

And you could unzip them with:

cd <save path>
unzip "*.zip"

For evaluating the methods on the KITTI (Eigen raw test set), you should further generate the ground-truth depth file by (as done in the Monodepth2):

python datasets/utils/export_kitti_gt_depth.py --data_path <root of KITTI> --split raw

If you want to evaluate the method on the KITTI improved test set, you should download the annotated depth maps (about 15GB) at Here and unzip it. Then you could generate the imporved ground-truth depth file by:

python datasets/utils/export_kitti_gt_depth.py --data_path <root of KITTI> --split improved

As an alternative, we provide the Eigen test subset (with .png images Here or with .jpg images Here, about 2GB) and the generated gt_depth files for the people who just want to do the evaluation.

KITTI Stereo 2015

For evaluating the model on the KITTI Stereo 2015 training set as many stereo matching methods, you should download the corresponding dataset Here and unzip it. It is noted that the training of the model also requires the entire KITTI dataset.

Evaluate the methods

To evaluate the methods on the prepared dataset, you could simply use

python evaluate.py\
 --exp_opts <path to the method EVALUATION option>\
 --model_path <path to the downloaded or trained model>

We provide the EVALUATION option files in options/<Method Name>/eval/*. Here we introduce some important arguments.

The output files are saved in eval_res\ by default. Please check evaluate.py for more information about arguments.

todo

You can reproduce our results on the KITTI Eigen test set by:

python evaluate.py\
 --exp_opts options/TiO-Depth/eval/tio_depth-swint-m_384_kitti.yaml\
 --model_path pretrained_models/TiO-Depth_384_bs8/model/last_model.pth

python evaluate.py\
 --exp_opts options/TiO-Depth/eval/tio_depth-swint-m_384_kitti.yaml\
 --model_path pretrained_models/TiO-Depth_384_bs8/model/last_model.pth\
 -fpp

python evaluate.py\
 --exp_opts options/TiO-Depth/eval/tio_depth-swint-m_384_kittistereo.yaml\
 --model_path pretrained_models/TiO-Depth_384_bs8/model/last_model.pth

You can reproduce our results on the KITTI 2015 training set by:

python evaluate.py\
 --exp_opts options/TiO-Depth/eval/tio_depth-swint-m_384_kitti2015stereo.yaml\
 --model_path pretrained_models/TiO-Depth_384_bs8_kittifull/model/last_model.pth\
 --metric_name depth_kitti_stereo2015

python evaluate.py\
 --exp_opts options/TiO-Depth/eval/tio_depth-swint-m_384_kitti2015.yaml\
 --model_path pretrained_models/TiO-Depth_384_bs8_kittifull/model/last_model.pth\
 --metric_name depth_kitti_stereo2015

python evaluate.py\
 --exp_opts options/TiO-Depth/eval/tio_depth-swint-m_384_kitti2015.yaml\
 --model_path pretrained_models/TiO-Depth_384_bs8_kittifull/model/last_model.pth\
 --metric_name depth_kitti_stereo2015\
 -fpp

Train the methods

Plese firstly download the pretrained Swin-trainsformer (Tiny Size) in their official repo and don't forget to set the path in path_my.py to the downloaded model. Then, you could train TiO-Depth simply use the commands provided in options/TiO-Depth/train/train_scripts.sh.

Acknowledgment

Mmsegmentation
Mmcv
Mmengine
PaddleSeg
Monodepth2
FAL-Net
KITTI Dataset