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SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud

By Bichen Wu, Alvin Wan, Xiangyu Yue, Kurt Keutzer (UC Berkeley)

This repository contains a tensorflow implementation of SqueezeSeg, a convolutional neural network model for LiDAR segmentation. A demonstration of SqueezeSeg can be found below:

<p align="center"> <img src="https://github.com/BichenWuUCB/SqueezeSeg/raw/master/readme/pr_0005.gif" width="600" /> </p>

Please refer to our video for a high level introduction of this work: https://youtu.be/Xyn5Zd3lm6s. For more details, please refer to our paper: https://arxiv.org/abs/1710.07368. If you find this work useful for your research, please consider citing:

@article{wu2017squeezeseg,
    title={Squeezeseg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3d lidar point cloud},
    author={Wu, Bichen and Wan, Alvin and Yue, Xiangyu and Keutzer, Kurt},
    journal={ICRA},
    year={2018}
}
@inproceedings{wu2018squeezesegv2,
    title={SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud},
    author={Wu, Bichen and Zhou, Xuanyu and Zhao, Sicheng and Yue, Xiangyu and Keutzer, Kurt},
    booktitle={ICRA},
    year={2019},
}
@inproceedings{yue2018lidar,
    title={A lidar point cloud generator: from a virtual world to autonomous driving},
    author={Yue, Xiangyu and Wu, Bichen and Seshia, Sanjit A and Keutzer, Kurt and Sangiovanni-Vincentelli, Alberto L},
    booktitle={ICMR},
    pages={458--464},
    year={2018},
    organization={ACM}
}

We recently open-sourced the code for SqueezeSegV2, a follow-up work to SqueezeSeg with significantly improved performance. For details, please check out: https://github.com/xuanyuzhou98/SqueezeSegV2

License

SqueezeSeg is released under the BSD license (See LICENSE for details). The dataset used for training, evaluation, and demostration of SqueezeSeg is modified from KITTI raw dataset. For your convenience, we provide links to download the converted dataset, which is distrubited under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License.

Installation:

The instructions are tested on Ubuntu 16.04 with python 2.7 and tensorflow 1.0 with GPU support.

Demo:

Training/Validation