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360SD-Net

project page | paper | dataset

This is the implementation of our ICRA 2020 paper "360° Stereo Depth Estimation with Learnable Cost Volume" by Ning-Hsu Wang

Overview

How to Use

• Setup a directory for all experiments. All you have to do in advance may look like this,

# SETUP REPO
>> git clone https://github.com/albert100121/360SD-Net.git
>> cd 360SD-Net
>> mkdir output
>> cd conda_env
>> conda create --name 360SD-Net python=2.7
>> conda activate 360SD-Net
>> conda install --file requirement.txt

# DOWNLOAD MP3D Dataset
>> cd ./data
# reqest download MP3D Dataset
>> unzip MP3D Dataset
# request download SF3D Dataset
>> unzip SF3D Dataset

• Setup data and directories (opt to you as long as the data is linked correctly). Set the directory structure for data as follows:

# MP3D Dataset
./data/
     |--MP3D/
                 |--train/
                       |--image_up/
                       |--image_down/
                       |--disp_up/
                 |--val/
                       |--image_up/
                       |--image_down/
                       |--disp_up/
                 |--test/
                       |--image_up/
                       |--image_down/
                       |--disp_up/
# SF3D Dataset
./data/
     |--SF3D/
                 |--train/
                       |--image_up/
                       |--image_down/
                       |--disp_up/
                 |--val/
                       |--image_up/
                       |--image_down/
                       |--disp_up/
                 |--test/
                       |--image_up/
                       |--image_down/
                       |--disp_up/

• Training procedure:

# For MP3D Dataset
>> python main.py --datapath data/MP3D/train/ --datapath_val data/MP3D/val/ --batch 8

# For SF3D Dataset
>> python main.py --datapath data/SF3D/train/ --datapath_val data/SF3D/val/ --batch 8 --SF3D

• Testing prodedure:

# For MP3D Dataset
>> python testing.py --datapath data/MP3D/test/ --checkpoint checkpoints/MP3D_checkpoint/checkpoint.tar --outfile output/MP3D

# For SF3D Dataset
>> python testing.py --datapath data/SF3D/test/ --checkpoint checkpoints/SF3D_checkpoint/checkpoint.tar --outfile output/SF3D

# For Real World Data
>> python testing.py --datapath data/realworld/ --checkpoint checkpoints/Realworld_checkpoint/checkpoint.tar --real --outfile output/realworld

# For small inference
>> python testing.py --datapath data/inference/MP3D/ --checkpoint checkpoints/MP3D_checkpoint/checkpoint.tar --outfile output/small_inference

• Disparity to Depth:

>> python utils/disp2de.py --path PATH_TO_DISPARITY

Notes

• The training process will cost a lot of GPU memory. Please make sure you have a GPU with 32G or larger memory.
• For testing, 1080Ti (12G) is enough for a 512 x 1024 image.

Synthetic Results

• Depth / Error Map <img src="img/qualitative_depth_error.png" width="600">
• Projected PCL <img src="img/qualitative_pcl.png" width="600">

Real-World Results

• Camera Setting <img src="img/camera_setting.jpg" width="600">
• Real World Results <img src="img/qualitative_realworld.png" width="600">

Citation

@article{wang2019360sdnet,
	title={360SD-Net: 360° Stereo Depth Estimation with Learnable Cost Volume},
	author={Ning-Hsu Wang and Bolivar Solarte and Yi-Hsuan Tsai and Wei-Chen Chiu and Min Sun},
	journal={arXiv preprint arXiv:1911.04460},
	year={2019}
}