tamwaiban / HR-Depth

[AAAI 2021] HR-Depth : High Resolution Self-Supervised Depth Estimation

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

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.

Qualitative_Result

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.

Quantitative Results

HR-Depth Results

Quantitative_results_1

Lite-HR-Depth Results

Quantitative_result_2

Usage

Requirements

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

Pretrained Model

We provided pretrained model as follow:

Model Name Resolution Dataset Supervision Abs_Rel $\delta<1.25$ $\delta<1.25^2$ $\delta<1.25^3$
HR_Depth_CS_K_MS_$640\times192$ $640\times192$ CS+K MS 0.104 0.893 0.964 0.983
HR_Depth_K_MS_$1024\times320$ $1024\times320$ K MS 0.101 0.899 0.966 0.983
HR_Depth_K_M_$1280\times384$ $1280\times384$ K M 0.104 0.894 0.966 0.984
Lite_HR_Depth_K_T_$1280\times384$ $1280\times384$ K T 0.104 0.893 0.967 0.985

KITTI training data

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.

KITTI evaluation

--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

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[AAAI 2021] HR-Depth : High Resolution Self-Supervised Depth Estimation

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