By Chenfeng Xu, Bichen Wu, Zining Wang, Wei Zhan, Peter Vajda, Kurt Keutzer, and Masayoshi Tomizuka.
This repository contains a Pytorch implementation of SqueezeSegV3, a state-of-the-art model for LiDAR segmentation. The framework of our SqueezeSegV3 can be found below:
Selected quantitative results of different approaches on the SemanticKITTI dataset (* means KNN post-processing):
Method | mIoU | car | bicycle | motorcycle | truck | person | bicyclist | motorcyclist | road |
---|---|---|---|---|---|---|---|---|---|
SqueezeSeg | 29.5 | 68.8 | 16.0 | 4.1 | 3.3 | 12.9 | 13.1 | 0.9 | 85.4 |
SqueezeSegV2 | 39.7 | 81.8 | 18.5 | 17.9 | 13.4 | 20.1 | 25.1 | 3.9 | 88.6 |
RangeNet21 | 47.4 | 85.4 | 26.2 | 26.5 | 18.6 | 31.8 | 33.6 | 4.0 | 91.4 |
RangeNet53 | 49.9 | 86.4 | 24.5 | 32.7 | 25.5 | 36.2 | 33.6 | 4.7 | 91.8 |
SqueezeSegV3-21 | 48.8 | 84.6 | 31.5 | 32.4 | 11.3 | 39.4 | 36.1 | 21.3 | 90.8 |
SqueezeSegV3-53 | 52.9 | 87.4 | 35.2 | 33.7 | 29.0 | 41.8 | 39.1 | 20.1 | 91.8 |
SqueezeSegV3-21* | 51.6 | 89.4 | 33.7 | 34.9 | 11.3 | 42.6 | 44.9 | 21.2 | 90.8 |
SqueezeSegV3-53* | 55.9 | 92.5 | 38.7 | 36.5 | 29.6 | 45.6 | 46.2 | 20.1 | 91.7 |
Visualization results of SqueezeSegV3:
For more details, please refer to our paper: SqueezeSegV3. The work is a follow-up work to SqueezeSeg, SqueezeSegV2 and LATTE. If you find this work useful for your research, please consider citing:
@inproceedings{xu2020squeezesegv3,
title={Squeezesegv3: Spatially-adaptive convolution for efficient point-cloud segmentation},
author={Xu, Chenfeng and Wu, Bichen and Wang, Zining and Zhan, Wei and Vajda, Peter and Keutzer, Kurt and Tomizuka, Masayoshi},
booktitle={European Conference on Computer Vision},
pages={1--19},
year={2020},
organization={Springer}
}
Related works:
@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{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},
booktitle={ICRA},
year={2018}
}
@inproceedings{wang2019latte,
title={LATTE: accelerating lidar point cloud annotation via sensor fusion, one-click annotation, and tracking},
author={Wang, Bernie and Wu, Virginia and Wu, Bichen and Keutzer, Kurt},
booktitle={2019 IEEE Intelligent Transportation Systems Conference (ITSC)},
pages={265--272},
year={2019},
organization={IEEE}
}
SqueezeSegV3 is released under the BSD license (See LICENSE for details).
The instructions are tested on Ubuntu 16.04 with python 3.6 and Pytorch 1.1.0 with GPU support.
- Clone the SqueezeSeg3 repository:
git clone https://github.com/chenfengxu714/SqueezeSegV3.git
- Use pip to install required Python packages:
pip install -r requirements.txt
- The SemanticKITTI dataset can be download here.
The pre-trained SqueezezSegV3-21 and SqueezeSegV3-53 are avaliable at Google Drive, you can directly download the two files.
We provide a demo script:
cd ./src/tasks/semantic/
python demo.py -m /path/to/model
You can find the prediction .label files and projected map in ./src/sample_output file, an example is shown below:
To infer the predictions for the entire dataset:
cd ./src/tasks/semantic/
python infer.py -d /path/to/dataset/ -l /path/for/predictions -m /path/to/model
To visualize the prediction for the sequence point cloud:
python visualize.py -d /path/to/dataset/ -p /path/to/predictions/ -s SQ_Number
cd ./src/tasks/semantic/
To train a network (from scratch):
python train.py -d /path/to/dataset -ac /config/arch/CHOICE.yaml -l /path/to/log
To train a network (from pretrained model):
python train.py -d /path/to/dataset -ac /config/arch/CHOICE.yaml -l /path/to/log -p /path/to/pretrained
We can monitor the training process using tensorboard.
tensorboard --logdir /file_path/
python evaluate_iou.py -d /path/to/dataset -p /path/to/predictions/ --split valid
We referred to RangeNet++ (Paper, Code) during our development. We thank the authors of RangeNet++ for open-sourcing their code.