RA-Depth

This repo is for RA-Depth: Resolution Adaptive Self-Supervised Monocular Depth Estimation (arxiv), ECCV2022

If you think it is a useful work, please consider citing it.

@inproceedings{he_ra_depth,
  title={RA-Depth: Resolution Adaptive Self-Supervised Monocular Depth Estimation},
  author={Mu, He and Le, Hui and Yikai, Bian and Jian, Ren and Jin, Xie and Jian, Yang},
  booktitle={ECCV},
  year={2022}
}

Overview of RA-Depth

Basic results on KITTI dataset

Visualization Results of Resolution Adaptation

Training:

CUDA_VISIBLE_DEVICES=0 python train.py --model_name RA-Depth --scales 0 --png --log_dir models --data_path /datasets/Kitti/Kitti_raw_data

Testing:

CUDA_VISIBLE_DEVICES=0 python evaluate_depth.py --load_weights_folder /models/RA-Depth/ --eval_mono --height 192 --width 640 --scales 0 --data_path /datasets/Kitti/Kitti_raw_data --png

Infer a single depth map from a RGB:

CUDA_VISIBLE_DEVICES=0 python test_simple.py --image_path /test.png --model_name RA-Depth

Environments:

python: 3.6.9
torch: 1.6.0

Acknowledgement

• The authors would like to thank Beibei Zhou and Kun Wang for their valuable suggestions and discussions.
• Thank the authors for their superior works: monodepth2, DIFFNet.