SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation. (ECCV2020)

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):

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}
}

License

SqueezeSegV3 is released under the BSD license (See LICENSE for details).

Installation

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.

Pre-trained Models

The pre-trained SqueezezSegV3-21 and SqueezeSegV3-53 are avaliable at Google Drive, you can directly download the two files.

Demo

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:

Inference

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

Training

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/

Evaluation

python evaluate_iou.py -d /path/to/dataset -p /path/to/predictions/ --split valid

Credits

We referred to RangeNet++ (Paper, Code) during our development. We thank the authors of RangeNet++ for open-sourcing their code.