UniFuse (RAL+ICRA2021)

Office source code of paper UniFuse: Unidirectional Fusion for 360${}^{\circ }^\backslash circ$ Panorama Depth Estimation, arXiv, Demo

Preparation

Installation

Environments

• python 3.6
• Pytorch >= 1.0.0
• CUDA >= 9.0

Install requirements

pip install -r requirements.txt

Datasets

Please download the preferred datasets, i.e., Matterport3D, Stanford2D3D, 3D60 and PanoSUNCG. For Matterport3D, please preprocess it following M3D-README.md.

Training

UniFuse on Matterport3D

python train.py --data_path $DATA_PATH \
-dataset matterport3d \
--model_name Matterport3D_UniFuse \
--batch_size 6 \
--num_epochs 100 \
--height 512 \
--width 1024 \
--imagenet_pretrained \
--net UniFuse 

Equirectangular baseline on Matterport3D

python train.py --data_path $DATA_PATH \
-dataset matterport3d \
--model_name Matterport3D_Equi \
--batch_size 6 \
--num_epochs 100 \
--height 512 \
--width 1024 \
--imagenet_pretrained \
--net Equi 

It is similar for other datasets.

Evaluation

Pre-trained models

The pre-trained models of UniFuse for 4 datasets are available, Matterport3D, Stanford2D3D, 3D60 and PanoSUNCG.

Test on a pre-trained model

python evaluate.py  --data_path $DATA_PATH --dataset matterport3d --load_weights_folder $MODEL_PATH 

Citation

Please cite our paper if you find our work useful in your research.

@article{jiang2021unifuse,
      title={UniFuse: Unidirectional Fusion for 360$^{\circ}$ Panorama Depth Estimation}, 
      author={Hualie Jiang and Zhe Sheng and Siyu Zhu and Zilong Dong and Rui Huang},
      journal={IEEE Robotics and Automation Letters},
      year={2021},
      publisher={IEEE}
}