SectorGSnet: Sector Learning for Efficient Ground Segmentation of Outdoor LiDAR Point Clouds

This is the official source code for SectorGSnet.

Dependencies

Python 3.7  
CUDA (tested on 10.2)
PyTorch (tested on 1.7)
argparse
numba

Data Preparation

We train our model using the SematicKITTI dataset. please find the SemanticKITTI dataset from their website.

Training & Testing

Hyper paramters for training to update in configuration file: /configs/sector_conf.yaml

python train_sector.py
python test_sector.py

Results

test

TODO

• Need to improve the reading speed of the point cloud
• The current version contains some test code and needs to be streamlined.

Acknowledgments

• This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No.2020-0-00440, Development of artificial intelligence technology that continuously improves itself as the situation changes in the real world).