Dense Pseudo-Labels with Dynamic Threshold for Semi-Supervised 3D Object Detection(DDS3D).
You can find the paper at https://arxiv.org/abs/2303.05079.
This is the repository for DDS3D(ICRA2023). In this repository, we provide DDS3D implementation (with pytorch) based on PV-RCNN and 3DIoUMatch.
i find some problems so this repo need to modify, i will fix them soon, please choose v2.0 branch.
Please refer to the origin README.md for installation and usage of OpenPCDet.
Please follow 3DIoUMatch-PVRCNN
For example
CUDA_VISIBLE_DEVICES=0,1,2,3 bash scripts/dist_pretrain.sh 4 --cfg_file cfgs/kitti_models/pvrcnn.yaml --extra_tag split_0.20_1 --split train_0.20_1 --ckpt_save_interval 4 --repeat 10 --dbinfos kitti_dbinfos_train_0.20_1_742.pklFor example
CUDA_VISIBLE_DEVICES=2,3 bash scripts/dist_train.sh 2 --cfg_file cfgs/kitti_models/pv_rcnn_ssl_db.yaml --split train_0.01_3 --extra_tag split_0.01_3 --ckpt_save_interval 2 --pretrained_model ../output/kitti_models/pvrcnn/split_0.01_3/ckpt/checkpoint_epoch_80.pth --repeat 5 --thresh '0.5,0.25,0.25' --sem_thresh '0.4,0.0,0.0' --dbinfos kitti_dbinfos_train_0.01_3_37.pklIf this work is helpful for your research, please consider citing the following BibTeX entry.
@INPROCEEDINGS{10160489,
author={Li, Jingyu and Liu, Zhe and Hou, Jinghua and Liang, Dingkang},
booktitle={2023 IEEE International Conference on Robotics and Automation (ICRA)},
title={DDS3D: Dense Pseudo-Labels with Dynamic Threshold for Semi-Supervised 3D Object Detection},
year={2023},
volume={},
number={},
pages={9245-9252},
doi={10.1109/ICRA48891.2023.10160489}}