CUDA 12.0
CUDNN 8.5.0
torch 1.7.1
torchvision 0.8.0
pip install -r LRRU/requirements.txt
pip3 install opencv-python
pip3 install opencv-python-headless
We used WANDB to visualize and track our experiments.
We used NVIDIA Apex for multi-GPU training as NLSPN.
Apex can be installed as follows:
$ cd PATH_TO_INSTALL
$ git clone https://github.com/NVIDIA/apex
$ cd apex
$ pip install -v --disable-pip-version-check --no-build-isolation --no-cache-dir ./
KITTI DC dataset is available at the KITTI DC Website and the data structure is:
.
├── depth_selection
│ ├── test_depth_completion_anonymous
│ │ ├── image
│ │ ├── intrinsics
│ │ └── velodyne_raw
│ ├── test_depth_prediction_anonymous
│ │ ├── image
│ │ └── intrinsics
│ └── val_selection_cropped
│ ├── groundtruth_depth
│ ├── image
│ ├── intrinsics
│ └── velodyne_raw
├── train
│ ├── 2011_09_26_drive_0001_sync
│ │ ├── image_02
│ │ │ └── data
│ │ ├── image_03
│ │ │ └── data
│ │ ├── oxts
│ │ │ └── data
│ │ └── proj_depth
│ │ ├── groundtruth
│ │ └── velodyne_raw
│ └── ...
└── val
├── 2011_09_26_drive_0002_sync
└── ...
$ sh train.sh
# download the pretrained model and add it to corresponding path.
$ sh val.sh
Thanks the ACs and the reviewers for their insightful comments, which are very helpful to improve our paper!
@inproceedings{wang2024improving,
title={Improving Depth Completion via Depth Feature Upsampling},
author={Wang, Yufei and Zhang, Ge and Wang, Shaoqian and Li, Bo and Liu, Qi and Hui, Le and Dai, Yuchao},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={21104--21113},
year={2024}
}