This is the official implementation for training and testing depth estimation using the model proposed in

HR-Depth: High Resolution Self-Supervised Monocular Depth Estimation

Xiaoyang Lyu, Liang Liu, Mengmeng Wang, Xin Kong, Lina Liu, Yong Liu*, Xinxin Chen and Yi Yuan.

This paper has been accepted by AAAI 2021.

Note: We temporarily release the evaluation version and some pretrained models of our paper. The training codes are modified according to Monodepth2, and we will release them soon.

Assuming a fresh Anaconda distribution, you can install the dependencies with:

conda install pytorch=1.5.0 torchvision=0.6.0 -c pytorch
conda install opencv=4.2
pip install scipy=1.4.1

We provided pretrained model as follow:

You can download the entire KITTI_raw dataset by running:

wget -i splits/kitti_archives_to_download.txt -P kitti_data/

Then unzip with

cd kitti_data
unzip "*.zip"
cd ..

Warning: The size of this dataset is about 175GB, so make sure you have enough space to unzip them.

--data_path: path of KITTI dataset --load_weights_folder: path of models --HR_Depth: inference by HR-Depth --Lite_HR_Depth: inference by Lite-HR-Depth

To prepare the ground truth depth maps run:

python export_gt_depth.py --data_path ./kitti_RAW

assuming that you have placed the KITTI RAW dataset in the default location of ./kitti_data.

For HR-Depth:

python evaluate_depth.py --data_path ./kitti_RAW --load_weights_folder ./models/HR_Depth_CS_K_MS_640x192 --HR_Depth

python evaluate_depth.py --data_path ./kitti_RAW --load_weights_folder ./models/HR_Depth_K_M_1280x384 --HR_Depth 

For Lite-HR-Depth:

python evaluate_depth.py --data_path ./kitti_RAW --load_weights_folder ./models/Lite_HR_Depth_K_T_1280x384 --Lite_HR_Depth