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[ECCV2022] 3D-PL: Domain Adaptive Depth Estimation with 3D-aware Pseudo-Labeling

[Paper] [Project Page]

Installation

• This code was developed with Python 3.7.10 & Pytorch 1.8.1 & CUDA 11.3
• Other requirements: numpy, cv2, tensorboardX
• Clone this repo

git clone https://github.com/ccc870206/3D-PL.git
cd 3D-PL

Dataset

Target dataset: KITTI

Rename the main folder of kitti dataset as kitti_data and put the folder under data/

data
  |----kitti_data 
         |----2011_09_26         
         |----2011_09_28        
         |----......... 

Source dataset: vKITTI (1.3.1)

Training (will release soon)

Testing

Download our pre-trained model and put the folder under checkpoints/.

• Test the model pre-trained with single-image setting

python3 test.py --model test --name best_model_single_image --which_epoch best

• Test the model pre-trained with stereo-pair setting

python3 test.py --model test --name best_model_stereo_pair --which_epoch best

Acknowledgments

Code is inspired by T^2Net and GASDA.