BayesianDeNet

This is the implementation of our paper "Bayesian DeNet: Monocular Depth Prediction and Frame-wise Fusion with Synchronized Uncertainty".

Demo Video:

Dependences

• Tensorflow
• OpenCV
• Numpy
• Eigen

Filelist.txt

rgb/00000000.png, depth/00000000.png
rgb/00000001.png, depth/00000001.png
rgb/00000002.png, depth/00000002.png
...

Test

Run

python predict.py --rgb_path='rgb.png' --model_path='model' --depth_path='depth.png' --confidence_path='confidence.png'

Train

To train the model, run

python train.py --filelist_path='filelist.txt' --pretrain_model_path='resnet50.npy' --output_models_dir='/home/xx/saved_models'

Data Augmentation

Run

python data_aug.py

Multi-frame Fusion

cd fusion  
mkdir build && cd build  
cmake ..  && make -j4
./fusion  

Citation

If you find this code useful, please cite:

@article{BayesianDeNet,
        title={Bayesian DeNet: Monocular Depth Prediction and Frame-wise Fusion with Synchronized Uncertainty},
        author={X. Yang and Y. Gao and H. Luo and C. Liao and K. Cheng},
        journal={IEEE Transactions on Multimedia},
        year = {2019},
}

Acknowledgement

We thank FCRN for their released code.