This is the official implementation of CVPR 2023 paper Viewpoint Equivariance for Multi-View 3D Object Detection authored by Dian Chen, Jie Li, Vitor Guizilini, Rares Ambrus, and Adrien Gaidon, at Toyota Research Institute. We introduce viewpoint equivariance on view-conditioned object queries achieving state-of-the-art 3D object performance.
- [May 4, 2023] Our code and models are released!
- [Mar. 27, 2023]
Our code and models will be released soon. Please stay tuned!
We provide instructions for using docker environment and pip/conda environment (docker is recommended for portability and reproducibility). Please refer to INSTALL.md for detailed instructions.
Please download the full NuScenes dataset from the official website, and preprocess the meta data following the instructions from MMDetection3D to obtain the .pkl
files with mmdet3d format. For convenience we provide the preprocessed .pkl
files for nuscenes dataset here. Put the .pkl
files under the NuScenes folder.
To train a model with the provided configs, please run the following:
# run distributed training with 8 GPUs
# tools/dist_train.sh <config path> 8 --work-dir <save dir> --cfg-options <overrides>
# for example:
tools/dist_train.sh projects/configs/vedet_vovnet_p4_1600x640_2vview_2frame.py 8 --work-dir work_dirs/vedet_vovnet_p4_1600x640_2vview_2frame/
Before running the training with V2-99 backbone, please download the DD3D pre-trained weights from here.
We provide results on the NuScenes val
set from the paper, as summarized below.
config | mAP | NDS | resolution | backbone | context | download |
---|---|---|---|---|---|---|
vedet_vovnet_p4_1600x640_2vview_2frame | 0.451 | 0.527 | 1600x640 | V2-99 | current + 1 past frame | model / log |
To run inference with a checkpoint, please run the following:
# run distributed evaluation with 8 GPUs
# tools/dist_test.sh <config path> <ckpt path> 8 --eval bbox
# for example:
tools/dist_test.sh projects/configs/vedet_vovnet_p4_1600x640_2vview_2frame.py ckpts/latest.pth 8 --eval bbox
You can evaluate the model following:
tools/dist_test.sh projects/configs/vedet_vovnet_p4_1600x640_2vview_2frame.py ckpts/latest.pth 1 --eval bbox
You can generate the reault json following:
./tools/dist_test.sh projects/configs/vedet_vovnet_p4_1600x640_2vview_2frame.py ckpts/latest.pth 1 --out ./pp-nus/results_eval.pkl --format-only --eval-options 'jsonfile_prefix=./pp-nus/results_eval'
You can visualize the 3D object detection following:
python3 tools/visualize.py
python tools/create_data.py nuscenes --version v1.0-mini --root-path ./data/nuscenes --out-dir ./data/nuscenes --extra-tag nuscenes
We release this repo under the CC BY-NC 4.0 license.
If you have any questions, feel free to open an issue under this repo, or contact us at [email protected]. If you find this work helpful to your research, please consider citing us:
@article{chen2023viewpoint,
title={Viewpoint Equivariance for Multi-View 3D Object Detection},
author={Chen, Dian and Li, Jie and Guizilini, Vitor and Ambrus, Rares and Gaidon, Adrien},
journal={arXiv preprint arXiv:2303.14548},
year={2023}
}
We also thank the authors of detr3d and petr/petrv2.
vedet's People
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