RA-Depth

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[ECCV2022] RA-Depth: Resolution Adaptive Self-Supervised Monocular Depth Estimation

RA-Depth 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# RA-Depth

This repo is for **[RA-Depth: Resolution Adaptive Self-Supervised Monocular Depth Estimation (arxiv)](https://arxiv.org/abs/2207.11984), 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
![](https://raw.githubusercontent.com/hmhemu/RA-Depth/main/assets/pipeline.png)

## Basic results on KITTI dataset
![](https://raw.githubusercontent.com/hmhemu/RA-Depth/main/assets/results1.png)

## Visualization Results of Resolution Adaptation
![](https://raw.githubusercontent.com/hmhemu/RA-Depth/main/assets/visuals.png)

## 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](https://github.com/nianticlabs/monodepth2), [DIFFNet](https://github.com/brandleyzhou/DIFFNet).

RA-Depth

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