Weihang Ran, Wei Yuan, Ryosuke Shibasaki
1.Download dataset from KITTI homepage
2.Organize the raw data and groundtruth as follows:
KITTI
├── raw_image
│ ├── train
│ │ ├── 2011_09_26_drive_0001_sync
│ │ ├── 2011_09_26_drive_0009_sync
│ │ ├── ...
│ ├── val
│ │ ├── 2011_09_26_drive_0002_sync
│ │ ├── 2011_09_26_drive_0005_sync
│ │ ├── ...
├── velodyne_raw
│ ├── train
│ │ ├── 2011_09_26_drive_0001_sync
│ │ ├── 2011_09_26_drive_0009_sync
│ │ ├── ...
│ ├── val
│ │ ├── 2011_09_26_drive_0002_sync
│ │ ├── 2011_09_26_drive_0005_sync
│ │ ├── ...
├── groundtruth
│ ├── train
│ │ ├── 2011_09_26_drive_0001_sync
│ │ ├── 2011_09_26_drive_0009_sync
│ │ ├── ...
│ ├── val
│ │ ├── 2011_09_26_drive_0002_sync
│ │ ├── 2011_09_26_drive_0005_sync
│ │ ├── ...
3.Run
python prepare_dataset.py --current_dir /path/to/current/data --target_dir /path/for/saving/preprocessed/data
After running this code, the preprocessed dataset should be organized like following:
KITTI
├── raw_image
│ ├── train
│ │ ├── 2011_09_26_drive_0001_sync_image_0000000000_image_02.png
│ │ ├── 2011_09_26_drive_0001_sync_image_0000000000_image_03.png
│ │ ├── ...
│ ├── val
│ │ ├── 2011_09_26_drive_0002_sync_image_0000000000_image_02.png
│ │ ├── 2011_09_26_drive_0002_sync_image_0000000000_image_03.png
│ │ ├── ...
├── velodyne_raw
│ ├── train
│ │ ├── 2011_09_26_drive_0001_sync_velodyne_raw_0000000005_image_02.png
│ │ ├── 2011_09_26_drive_0001_sync_velodyne_raw_0000000005_image_03.png
│ │ ├── ...
│ ├── val
│ │ ├── 2011_09_26_drive_0002_sync_velodyne_raw_0000000005_image_02.png
│ │ ├── 2011_09_26_drive_0001_sync_velodyne_raw_0000000005_image_03.png
│ │ ├── ...
├── groundtruth
│ ├── train
│ │ ├── 2011_09_26_drive_0001_sync_groundtruth_depth_0000000005_image_02.png
│ │ ├── 2011_09_26_drive_0001_sync_groundtruth_depth_0000000005_image_03.png
│ │ ├── ...
│ ├── val
│ │ ├── 2011_09_26_drive_0002_sync_groundtruth_depth_0000000005_image_02.png
│ │ ├── 2011_09_26_drive_0002_sync_groundtruth_depth_0000000005_image_03.png
│ │ ├── ...
conda env create -f environment.yaml
conda activate fsdc
git clone https://github.com/lucidrains/denoising-diffusion-pytorch.git
cd denoising-diffusion-pytorch
pip install -v -e .
git clone https://github.com/kakaxi314/GuideNet.git
cd GuideNet/exts
python setup.py install
accelerate launch main.py --path /Path/to/your/preprocessed_KITTI --mode train_ddpm
After training the checkpoint will be saved in ./results/
You can also directly download our pretrained DDPM checkpoint on KITTI dataset from here
accelerate launch main.py --path /Path/to/your/preprocessed_KITTI --mode train_fsdc --ddpm_ckpt /Path/to/trained/DDPM/checkpoint
After training the checkpoint will also be saved in ./results/
accelerate launch main.py --path /Path/to/your/preprocessed_KITTI --mode eval --ddpm_ckpt /Path/to/trained/DDPM/checkpoint --fsdc_ckpt /Path/to/trained/FSDC/checkpoint
If this repo is helpful to you, please cite our work:
@inproceedings{ran2023few,
title={Few-Shot Depth Completion Using Denoising Diffusion Probabilistic Model},
author={Ran, Weihang and Yuan, Wei and Shibasaki, Ryosuke},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={6558--6566},
year={2023}
}
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