PDV is LiDAR 3D object detection method. This repository is based off [OpenPCDet].
Point Density-Aware Voxels for LiDAR 3D Object Detection
Jordan S. K. Hu, Tianshu Kuai, Steven L. Waslander
[Paper]
[2022-03-07] PDV v0.1.0 is released.
Selected supported methods are shown in the below table. The results are the 3D detection performance of moderate difficulty on the val set of KITTI dataset.
- All models are trained with 2 NVIDIA Tesla P100 GPUs and are available for download.
- The training time is measured with 2 NVIDIA Tesla P100 GPUs and PyTorch 1.7.
| training time | Car@R40 | Pedestrian@R40 | Cyclist@R40 | log | download | |
|---|---|---|---|---|---|---|
| PDV | ~23 hours | 85.05 | 57.41 | 75.95 | log | model-147M |
We could not provide the above pretrained models due to Waymo Dataset License Agreement, but you could easily achieve similar performance by training with the default configs. PDV is trained with 10% data (~16k frames) on 4 NVIDIA Tesla V100s GPUs.
Please refer to INSTALL.md for the installation of PDV.
Please refer to DEMO.md for a quick demo to test with a pretrained model and visualize the predicted results on your custom data or the original KITTI data.
Please refer to GETTING_STARTED.md to learn more usage about this project.
PDV is released under the Apache 2.0 license.
We would like to thank the authors of OpenPCDet for their open source release of their codebase.
If you find this project useful in your research, please consider citing:
@article{PDV,
title={Point Density-Aware Voxels for LiDAR 3D Object Detection},
author={Jordan S. K. Hu and
Tianshu Kuai and
Steven L. Waslander},
journal={CVPR},
year={2022}
}