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