This repository provides a non-official implementation of the paper: DenseLiDAR: A Real-time Pseudo Dense Depth Guided Depth Completion Network
Jiaqi Gu. et al, DenseLiDAR: A Real-Time Pseudo Dense Depth Guided Depth Completion Network. ICRA 2021
We express our gratitude to Jiaqi Gu et al. for their groundbreaking work on DenseLiDAR. This implementation is inspired by their paper presented at ICRA 2021. We also appreciate the contributions of the open-source community and the resources provided by PyTorch.
This project was conducted as part of the Internship program at Mobiltech and VIP-Lab at Gachon University.
- Implementation by : 구도연, 김다혜, 조재현 [VIP-Lab at Gachon University.]
- Project Supervision : 정선재 [Mobiltech.]
Please note that this implementation is not the official code provided by the authors of the paper. Therefore, performance metrics obtained using our code may differ from those reported in the original paper due to differences in implementation details, parameter settings, and hardware.
Note: Our implementation results are not the same.
You can check our implementation results of Depth Completion/3D Visualization on this link.
Predictions
3D Visualizations
- Ubuntu 20.04 LTS
- Python 3.8
- CUDA 10.2, 11.08
git clone https://github.com/suunnnnnj/DenseLiDAR.git
cd DenseLiDAR
pip install -r requirements.txt
kitti lidar dataset download
Visit the KITTI homepage and download the two datasets below on the depth completion page.
- Download annotated depth maps data set (14 GB)
- Download projected raw LiDAR scans data set (5 GB)
kitti_raw dataset download
cd datasets/kitti_depth
wget https://github.com/youmi-zym/CompletionFormer/files/12575038/kitti_archives_to_download.txt
wget -i kitti_archives_to_download.txt -P kitti_raw/
cd kitti_raw
unzip "*.zip"
datasets
├──data_depth_annotated
| ├── train
| └── val
├── data_depth_velodyne
| ├── train
| └── val
└── kitti_raw
├── 2011_09_26
├── 2011_09_28
├── 2011_09_29
├── 2011_09_30
└── 2011_10_03
Dataset configuration
Download all three data, configure it as in the file structure above, and then run the commands below to configure the dataset to be used for learning.
Once completed, you will have the following file structure.
python submodules/dataset/dataset_settings.py
datasets
├──data_depth_annotated
| ├── train
| └── val
├── data_depth_velodyne
| ├── train
| └── val
├── kitti_raw
| ├── train
| └── val
├── pseudo_depth_map
| ├── train
| └── val
└── pseudo_gt_map
├── train
└── val
python train.py --data_path [YOUR_DATASET_PATH] --epochs [EPOCHS] --checkpoint [CHECKPOINT] --batch_size [BATCH_SIZE] --gpu_nums [YOUR_GPU_NUMS]
Arguments
--data_path
: your dataset path | default: None--epochs
: number of epochs to train | default: 40--checkpoint
: number of epochs to making checkpoint | default: 5--batch_size
: number of batch size to train | default: 1--gpu_nums
: number of gpus to train | default: 1
Example
python train.py --data_path datasets/ --epochs 40 --batch_size 16 --gpu_nums 4
python demo.py --model_path [YOUR_MODEL_PATH] --image_path [YOUR_IMAGE_PATH] --sparse_path [YOUR_LIDAR_POINT_PATH] --pseudo_depth_map_path [YOUR_PSEUDO_DEPTH_MAP_PATH] --output_path [YOUR_SAVE_PATH]
Arguments
--model_path
: your model path | default: None--image_path
: your raw image path | default: demo/demo_image.png--sparse_path
: your raw lidar path | default: demo/demo_velodyne.png--pseudo_depth_map_path
: your pseudo depth map path | default: demo/demo_pseudo_depth.png--output_path
: your save result path | default: demo/dense_depth_output.png
Example
python demo.py --model_path checkpoint/epoch-5_loss-3.273.tar --image_path demo/demo_image.png --sparse_path demo/demo_velodyne.png --pseudo_depth_map_path demo/demo_pseudo_depth.png --output_path demo/dense_depth_output.png
We express our sincere gratitude to the creators of the DeepLiDAR project. Their work provided a valuable foundation for our project. Additionally, we thank the authors of the DenseLiDAR paper. Our code is a non-official implementation based on their research.
Thank you all for your outstanding contributions and dedication to the open-source and research communities.
If you use our implementation or method in your work, please cite the following
@ARTICLE{9357967,
author={Gu, Jiaqi and Xiang, Zhiyu and Ye, Yuwen and Wang, Lingxuan},
journal={IEEE Robotics and Automation Letters},
title={DenseLiDAR: A Real-Time Pseudo Dense Depth Guided Depth Completion Network},
year={2021},
volume={6},
number={2},
pages={1808-1815},
keywords={Three-dimensional displays;Task analysis;Measurement;Laser radar;Real-time systems;Training;Object detection;Deep learning for visual perception;RGB-D perception;recognition},
doi={10.1109/LRA.2021.3060396}}