SCCKTIM
Weakly Supervised 3D Object Detection from Point Cloud with Only Image-Level Annotation
Our code will be available soon.
The class knowledge transfer module and pseudo_label generalization module provide docker images.
Class Knowledge Transfer Module
Installation according to WS3DOD.
Generating the superpixel by running the following:
conda activate ws3dod
cd core/source/context_module
python generate_superpixel_image
Our data file structure is as follows:
--data
--kitti
--training
--calib
--image_2
--label_2
--planes
--sphere
--superpixel_2
--velodyne
--train.txt
--trainval.txt
--kitti_pseudo
--training
--label_2
Files in kitti_pseudo are generated by PG in the previous iteration.
Please read core/launcher.py and paper for details of running the code.
Conceptual Knowledge Transfer Module
Following README.md in CKT
Pseudo-label Generalization
Installation according to OpenpcDet.
conda activate openpcdet
Our data file structure is as follows:
--data
--kitti
--ImageSets
--trainval.txt
--val.txt
--test.txt
--ImageSets_real
--train.txt
--trainval.txt
--val.txt
--test.txt
--testing
--calib
--image_2
--velodyne
--training
--calib
--image_2
--label_2
--velodyne
--planes
--pseudo_label
--waymo
Files in pseudo_label are generated by CKT previous step.
label_2 is empty before training the deep network. Using the following command to generate pseudo-labels:
cd tools
python generate_pseudo_label
Using the following command for training deep network.
python -m torch.distributed.launch --nproc_per_node=4 train.py --launcher pytorch --cfg_file cfgs/kitti_models/pv_rcnn.yaml│
--sync_bn --fix_random_seed --extra_tag normal_nonrot_pcn_reg_pvrcnn_iter1_pcn_reg
License
We note that some code in this repository is adapted from the following repositories: