Science Score: 54.0%

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Repository

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  • Host: GitHub
  • Owner: Elaine-Blue
  • License: other
  • Language: Python
  • Default Branch: main
  • Size: 15.6 MB
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Created over 1 year ago · Last pushed over 1 year ago
Metadata Files
Readme License Citation

README.md

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Forward-Backward View Transformation for Vision-Centric AV Perception

Paper (FB-BEV) | Paper (FB-OCC) | Intro Video

FB-BEV and FB-OCC are a family of vision-centric 3D object detection and occupancy prediction methods based on forward-backward view transformation.

News

  • [2023/8/01] FB-BEV was accepted to ICCV 2023.
  • 🏆 [2023/6/16] FB-OCC wins both Outstanding Champion and Innovation Award in Autonomous Driving Challenge in conjunction with CVPR 2023 End-to-End Autonomous Driving Workshop and Vision-Centric Autonomous Driving Workshop.

Getting Started

Model Zoo

| Backbone | Method | Lr Schd | IoU| Config | Download | | :---: | :---: | :---: | :---: | :---: | :---: | | R50 | FB-OCC | 20ep | 39.1 |config |model|

  • More model weights will be released later.

License

Copyright © 2022 - 2023, NVIDIA Corporation. All rights reserved.

This work is made available under the Nvidia Source Code License-NC. Click here to view a copy of this license.

The pre-trained models are shared under CC-BY-NC-SA-4.0. If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.

For business inquiries, please visit our website and submit the form: NVIDIA Research Licensing.

Citation

If this work is helpful for your research, please consider citing:

@inproceedings{li2023fbbev, title={{FB-BEV}: {BEV} Representation from Forward-Backward View Transformations}, author={Li, Zhiqi and Yu, Zhiding and Wang, Wenhai and Anandkumar, Anima and Lu, Tong and Alvarez, Jose M}, booktitle={IEEE/CVF International Conference on Computer Vision (ICCV)}, year={2023} }

@article{li2023fbocc, title={{FB-OCC}: {3D} Occupancy Prediction based on Forward-Backward View Transformation}, author={Li, Zhiqi and Yu, Zhiding and Austin, David and Fang, Mingsheng and Lan, Shiyi and Kautz, Jan and Alvarez, Jose M}, journal={arXiv:2307.01492}, year={2023} }

Acknowledgement

Many thanks to these excellent open source projects:

- BEVFormer, BEVDet, Occ3D, OpenOccupancy, SoloFusion

FB-BEV-TRT

a2921a53bc71464f26d281eb8106ce231661b17a

Owner

  • Name: Elaine_Blue
  • Login: Elaine-Blue
  • Kind: user

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - name: "MMDetection3D Contributors"
title: "OpenMMLab's Next-generation Platform for General 3D Object Detection"
date-released: 2020-07-23
url: "https://github.com/open-mmlab/mmdetection3d"
license: Apache-2.0

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Dependencies

mmdet3d/ops/ops_dcnv3/setup.py pypi
requirements/build.txt pypi
requirements/docs.txt pypi
  • docutils ==0.16.0
  • m2r *
  • mistune ==0.8.4
  • myst-parser *
  • sphinx ==4.0.2
  • sphinx-copybutton *
  • sphinx_markdown_tables *
requirements/mminstall.txt pypi
  • mmcv-full >=1.4.8,<=1.6.0
  • mmdet >=2.24.0,<=3.0.0
  • mmsegmentation >=0.20.0,<=1.0.0
requirements/optional.txt pypi
  • open3d *
  • spconv *
  • waymo-open-dataset-tf-2-1-0 ==1.2.0
requirements/readthedocs.txt pypi
  • mmcv >=1.4.8
  • mmdet >=2.24.0
  • mmsegmentation >=0.20.1
  • torch *
  • torchvision *
requirements/runtime.txt pypi
  • lyft_dataset_sdk *
  • networkx >=2.2,<2.3
  • numba ==0.53.0
  • numpy *
  • nuscenes-devkit *
  • plyfile *
  • scikit-image *
  • tensorboard *
  • trimesh >=2.35.39,<2.35.40
requirements/tests.txt pypi
  • asynctest * test
  • codecov * test
  • flake8 * test
  • interrogate * test
  • isort * test
  • kwarray * test
  • pytest * test
  • pytest-cov * test
  • pytest-runner * test
  • ubelt * test
  • xdoctest >=0.10.0 test
  • yapf * test
requirements.txt pypi
setup.py pypi