StereoNet ActiveStereoNet Save

X-StereoLab is an open source stereo matching and stereo 3D object detection toolbox based on PyTorch.

News: We released the codebase v0.0.0.

• matching and detection model result.

• GOOGLE HITNET model pytorch model will be released.

• GOOGLE HITNET model pytorch KITTI2015 submission: http://www.cvlibs.net/datasets/kitti/eval_scene_flow_detail.php?benchmark=stereo&result=226494ba5559e9f5f46bdbd681d1564fee78409e ranking 145 with 80GMAC

Requirements

All the codes are tested in the following environment:

• Ubuntu 16.04
• Python 3.7
• PyTorch 1.1.0 or 1.2.0 or 1.3.0
• Torchvision 0.2.2 or 0.4.1

Installation

(1) Clone this repository.

git clone [email protected]:meteorshowers/X-StereoLab.git && cd X-StereoLab

(2) Setup Python environment.

conda activate -n xstereolab
pip install -r requirements.txt --user

## conda deactivate xstereolab

Data Preparation

(1) Please download the KITTI dataset.

ln -s /path/to/KITTI_DATA_PATH ./data/kitti/
ln -s /path/to/OUTPUT_PATH ./outputs/

Multi-GPU Training

The training scripts support multi-processing distributed training, which is much faster than the typical PyTorch DataParallel interface.

python3 tools/train_net_disp.py --cfg ./configs/config_xxx.py --savemodel ./outputs/MODEL_NAME -btrain 4 -d 0-3 --multiprocessing-distributed

The training models, configuration and logs will be saved in the model folder.

To load some pretrained model, you can run

python3 tools/train_net_disp.py --cfg xxx/config.py --loadmodel ./outputs/MODEL_NAMEx --start_epoch xxx --savemodel ./outputs/MODEL_NAME -btrain 4 -d 0-3 --multiprocessing-distributed

If you want to continue training from some epochs, just set the cfg, loadmodel and start_epoch to the respective model path.

Besides, you can start a tensorboard session by

tensorboard --logdir=./outputs/MODEL_NAME/tensorboard --port=6666

and visualize your training process by accessing https://localhost:6666 on your browser.

Inference and Evaluation

on working ...

stereo matching Performance and Model Zoo

</tbody>

Methods	Epochs	Train Mem (GB/Img)	Test Mem (GB/Img)	EPE	D1-all	Models
HITNET (kitti)	4200				2.43%	GoogleDrive
HITNET (sceneflow)	200			0.65		GoogleDrive
stereonet (sceneflow)	20			1.10		GoogleDrive
ActiveStereoNet	10					GoogleDrive
SOS

stereo 3D detection Performance and Model Zoo

PLUME: Efficient 3D Object Detection from Stereo Images

Methods	Epochs	Train Mem (GB/Img)	Test Mem (GB/Img)	3D BEV AP (Ours small plume)	3D BEV AP (Paper small plume)
PLUME				72.9 / 62.5 / 56.9	74.4 / 61.7 / 55.8

Video Demo

We provide a video demo for showing the result of X-StereoLab. Here we show the predicted disparity map of activastereonet.

TODO List

• Multiprocessing GPU training
• TensorboardX
• Reduce training GPU memory usage
• eval and test code
• Result visualization
• Still in progress

Citations

If you find our work useful in your research, please consider citing:

@misc{XStereoLab2021,
    title={{X-StereoLab} stereo matching and stereo 3D object detection toolbox},
    author={X-StereoLab Contributors},
    howpublished = {\url{https://github.com/meteorshowers/X-StereoLab}},
    year={2021}
}
* refercence[2] 
@article{tankovich2020hitnet,
  title={HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching},
  author={Tankovich, Vladimir and H{\"a}ne, Christian and Fanello, Sean and Zhang, Yinda and Izadi, Shahram and Bouaziz, Sofien},
  journal={arXiv preprint arXiv:2007.12140},
  year={2020}
}

* refercence[3] 
@inproceedings{tankovich2018sos,
  title={Sos: Stereo matching in o (1) with slanted support windows},
  author={Tankovich, Vladimir and Schoenberg, Michael and Fanello, Sean Ryan and Kowdle, Adarsh and Rhemann, Christoph and Dzitsiuk, Maksym and Schmidt, Mirko and Valentin, Julien and Izadi, Shahram},
  booktitle={2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages={6782--6789},
  year={2018},
  organization={IEEE}
}

Others contributors

vtankovich ​ GOOGLE ​

Yan Wang ​ Waymo ​

Acknowledgment

• Thanks to SamehKhamis (NVIDIA)

License

The code is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License for NonCommercial use only. Any commercial use should get formal permission first.

Contact

If you have any questions or suggestions about this repo, please feel free to contact me ([email protected]). Wechat:

XUANYILI ​ ​