a lightweight version of SECOND LiDAR object detection

my change:

• use own customized spconv_lite instead of spconv
• rewrite/refactor code
• use a simplfied voxel extractor, but works well.
• trained with kitti
• trained with lyft 3d detection
• add my own understanding and illustrations about the model
• test on a real testing car, 0.05 sec per Lidar frame, wth GTX 2080

My results

• KITTI 2011-09-26-0005
• KITTI 2011 09 26 0023
• test on a real testing car
  • 3d visualization is my opengl 2.0 practice repo
  • results currently rendered in BEV, yellow is cyclist, defined in KITTI

this is a showcase repo, KITTI training sketch code can be found in https://github.com/masszhou/SECOND_lite_dev

under root of this project

python -m script.predict_kitti predict_pcl_files --pcl_path="{kitti_root}/2011_09_26/2011_09_26_drive_0023_sync/velodyne_points/data/" --image_root_path="{kitti_root}/2011_09_26/2011_09_26_drive_0023_sync/image_02/data/" --save=True

training with 180° FOV labels, inference with 360° FOV

• green: car
• blue: van
• red: pedestrian
• yellow: cyclist

My results

• KITTI 2011-09-26-0005
• KITTI 2011 09 26 0023