It is the official code release of HEDNet on the nuScenes dataset.
We implemented HEDNet on NuScenes based on mmdetection3d, because the TransFusion-L implemented on OpenPCDet achieved lower accuracy than on mmdetection3d. We will unify the code in the future.
| Model | mATE | mASE | mAOE | mAVE | mAAE | mAP | NDS | download |
|---|---|---|---|---|---|---|---|---|
| HEDNet | 27.5 | 25.1 | 26.3 | 23.3 | 18.7 | 67.0 | 71.4 | ckpt |
| Model | mATE | mASE | mAOE | mAVE | mAAE | mAP | NDS | download |
|---|---|---|---|---|---|---|---|---|
| HEDNet | 25.0 | 23.8 | 31.7 | 24.0 | 13.0 | 67.5 | 72.0 | json |
Please refer to getting_started for installation, usage for usage. We used python 3.8, pytorch 1.10, cuda11.3, spconv-cu113 2.3.3, mmdet3d 0.18.1, mmdet 2.11.0, and mmcv 1.3.13.
@inproceedings{
zhang2023hednet,
title={{HEDN}et: A Hierarchical Encoder-Decoder Network for 3D Object Detection in Point Clouds},
author={Gang Zhang and Chen Junnan and Guohuan Gao and Jianmin Li and Xiaolin Hu},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
}
This work was supported in part by the National Key Research and Development Program of China (No. 2021ZD0200301) and the National Natural Science Foundation of China (Nos. U19B2034, 61836014) and THU-Bosch JCML center.