This is a PyTorch(0.4.1) implementation of Deeper Depth Prediction with Fully Convolutional Residual Networks. It can use Fully Convolutional Residual Networks to realize monocular depth prediction. Currently, we can train FCRN using NYUDepthv2 and Kitti Odometry Dataset.
The code was tested with Python 3.5 with Pytorch 0.4.1 in 12GB TITAN X. We train 60 epochs with batch size = 16. The trained model can be download from BaiduYun.
| Method | rml | rmse | log10 | Delta1 | Delta2 | Delta3 |
|---|---|---|---|---|---|---|
| FCRN | 0.127 | 0.573 | 0.055 | 0.811 | 0.953 | 0.988 |
| FCRN_ours | 0.149 | 0.527 | 0.062 | 0.805 | 0.954 | 0.987 |
| Method | rml | rmse | log10 | Delta1 | Delta2 | Delta3 |
|---|---|---|---|---|---|---|
| FCRN_ours | 0.113 | 4.801 | 0.048 | 0.865 | 0.957 | 0.984 |
The code was tested with Python 3.5 with Pytorch 0.4.1 in 2 GPU TITAN X.
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Clone the repo:
git clone git@github.com:dontLoveBugs/FCRN_pyotrch.git cd FCRN_pytorch -
Install dependencies:
For PyTorch dependency, see pytorch.org for more details.
For custom dependencies:
pip install matplotlib pillow tensorboardX
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Configure your dataset path in "dataloaders/path.py".
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Training
To train NYU Depth v2, please do:
python main.py --dataset nyu
To train it on KITTI, please do:
python main.py --dataset kitti