A Concise but High-performing Network for Image Guided Depth Completion in Autonomous Driving

This repository is the implementation of our paper A Concise but High-performing Network for Image Guided Depth Completion in Autonomous Driving.

Demo

Results

Dependent Environment

You can refer to the following environment:

• python=3.6.2
• torch==1.9.0+cu111
• torchvision==0.10.0+cu111

pip install numpy matplotlib Pillow
pip install scikit-image
pip install opencv-contrib-python

Data

• Download the KITTI Depth Dataset from their website. Use the following scripts to extract corresponding RGB images from the raw dataset.

./download/rgb_train_downloader.sh
./download/rgb_val_downloader.sh

The downloaded rgb files will be stored in the ../data/data_rgb folder. The overall code, data, and results directory is structured as follows.

├── CHNet
├── data
|   ├── data_depth_annotated
|   |   ├── train
|   |   ├── val
|   ├── data_depth_velodyne
|   |   ├── train
|   |   ├── val
|   ├── depth_selection
|   |   ├── test_depth_completion_anonymous
|   |   ├── test_depth_prediction_anonymous
|   |   ├── val_selection_cropped
|   └── data_rgb
|   |   ├── train
|   |   ├── val
├── results

Train

You can train the CHNet through the following command:

python main.py -b 8 (8 is a example of batch size)

Evalution

You can evaluate the CHNet through the following command:

python main.py -b 1 --evaluate [checkpoint-path]

Test

You can test the CHNet through the following command for online submission:

python main.py -b 1 --evaluate [checkpoint-path] --test

Acknowledgement

Many thanks to these excellent opensource projects

• PENet
• GuideNet
• self-supervised-depth-completion

Citation

Please consider citing my work as follows if it is helpful for you.

@article{liu2024concise,
  title={A concise but high-performing network for image guided depth completion in autonomous driving},
  author={Liu, Moyun and Chen, Bing and Chen, Youping and Xie, Jingming and Yao, Lei and Zhang, Yang and Zhou, Joey Tianyi},
  journal={Knowledge-Based Systems},
  pages={111877},
  year={2024},
  publisher={Elsevier}
}