We proposed a multi-scale attentive aggregation network for semantic segmentation of LiDAR point cloud. We test our network on two datasets which are CSPC dataset and Tonronto3D dataset.
conda create -n randlanet python=3.5
source activate randlanet
pip install -r helper_requirements.txt
sh compile_op.sh
We put the preprocessed data in /data/cspc and /data/toronto3d.
cspc:python -B main_CSPC.py --gpu 0 --mode train --test_area 5
toronto3d:python -B main_toronto3d.py --gpu 0 --mode train --test_area 5
cspc:python -B main_CSPC.py --gpu 0 --mode test --test_area 5
toronto3d:python -B main_toronto3d.py --gpu 0 --mode test --test_area 5