We test this project on NVIDIA A100 GPUs and Ubuntu 18.04.
conda create -n pvt-ssd python=3.7
conda activate pvt-ssd
conda install -y pytorch==1.10.1 torchvision==0.11.2 cudatoolkit=11.1 -c pytorch -c conda-forge
conda install -y -c fvcore -c iopath -c conda-forge fvcore iopath
conda install -y pytorch3d -c pytorch3d
pip install numpy==1.19.5 protobuf==3.19.4 scikit-image==0.19.2 waymo-open-dataset-tf-2-2-0 nuscenes-devkit==1.0.5 einops==0.6.0 spconv-cu111 numba scipy pyyaml easydict fire tqdm shapely matplotlib opencv-python addict pyquaternion awscli open3d pandas future pybind11 tensorboardX tensorboard Cython
pip install torch-scatter -f https://data.pyg.org/whl/torch-1.10.1+cu111.html
git clone https://github.com/Nightmare-n/PVT-SSD
cd PVT-SSD && python setup.py develop --user
Please follow the instruction of OpenPCDet to prepare the dataset. For the Waymo dataset, we use the evaluation toolkits to evaluate detection results.
data
│── waymo
│ │── ImageSets/
│ │── raw_data
│ │ │── segment-xxxxxxxx.tfrecord
│ │ │── ...
│ │── waymo_processed_data
│ │ │── segment-xxxxxxxx/
│ │ │── ...
│ │── waymo_processed_data_gt_database_train_sampled_1/
│ │── waymo_processed_data_waymo_dbinfos_train_sampled_1.pkl
│ │── waymo_processed_data_infos_test.pkl
│ │── waymo_processed_data_infos_train.pkl
│ │── waymo_processed_data_infos_val.pkl
│ │── compute_detection_metrics_main
│ │── gt.bin
│── kitti
│ │── ImageSets/
│ │── training
│ │ │── label_2/
│ │ │── velodyne/
│ │ │── ...
│ │── testing
│ │ │── velodyne/
│ │ │── ...
│ │── gt_database/
│ │── kitti_dbinfos_train.pkl
│ │── kitti_infos_test.pkl
│ │── kitti_infos_train.pkl
│ │── kitti_infos_val.pkl
│ │── kitti_infos_trainval.pkl
│── once
│ │── ImageSets/
│ │── data
│ │ │── 000000/
│ │ │── ...
│ │── gt_database/
│ │── once_dbinfos_train.pkl
│ │── once_infos_raw_large.pkl
│ │── once_infos_raw_medium.pkl
│ │── once_infos_raw_small.pkl
│ │── once_infos_train.pkl
│ │── once_infos_val.pkl
│── kitti-360
│ │── data_3d_raw
│ │ │── xxxxxxxx_sync/
│ │ │── ...
│── ckpts
│ │── pvt_ssd.pth
│ │── ...
# train
bash scripts/dist_train.sh
# test
bash scripts/dist_test.sh
Vec_L1 | Vec_L2 | Ped_L1 | Ped_L2 | Cyc_L1 | Cyc_L2 | Model | |
---|---|---|---|---|---|---|---|
PVT-SSD | 79.2/78.7 | 70.2/69.8 | 79.9/74.0 | 72.6/67.0 | 77.1/76.0 | 74.0/73.0 | log |
PVT-SSD_3f | 80.6/80.2 | 71.9/71.5 | 83.9/80.6 | 75.1/72.1 | 77.9/77.0 | 74.8/74.0 | log |
We could not provide the above pretrained models due to Waymo Dataset License Agreement.
If you find this project useful in your research, please consider citing:
@inproceedings{yang2023pvtssd,
author = {Yang, Honghui and Wang, Wenxiao and Chen, Minghao and Lin, Binbin and He, Tong and Chen, Hua and He, Xiaofei and Ouyang, Wanli},
title = {PVT-SSD: Single-Stage 3D Object Detector With Point-Voxel Transformer},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2023},
pages = {13476-13487}
}
This project is mainly based on the following codebases. Thanks for their great works!