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geobev

This is the implementation of the paper "GeoBEV: Learning Geometric BEV Representation for Multi-view 3D Object Detection" (AAAI25)

https://github.com/mengtan00/geobev

Science Score: 54.0%

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Last synced: 6 months ago · JSON representation ·

Repository

This is the implementation of the paper "GeoBEV: Learning Geometric BEV Representation for Multi-view 3D Object Detection" (AAAI25)

Basic Info
  • Host: GitHub
  • Owner: mengtan00
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 5.37 MB
Statistics
  • Stars: 9
  • Watchers: 2
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 1 year ago · Last pushed about 1 year ago
Metadata Files
Readme License Citation

README.md

GeoBEV

[AAAI2025] GeoBEV: Learning Geometric BEV Representation for Multi-view 3D Object Detection


News

  • GeoBEV is accepted by AAAI 2025. The paper is available at arxiv.

Main Results

| Config | mAP | NDS | Download | | --------------------------------------------------------------- | :----: | :----: | :---: | | GeoBEV-R50-nuImage-CBGS | 0.430 | 0.546 | model | | GeoBEV-R50-nuImage-CBGS-Longterm | 0.479 | 0.575 | model | | GeoBEV-R101-nuImage-CBGS-Longterm| 0.526 | 0.615 | model |

Get Started

1. Please follow these steps to install GeoBEV.

a. Create a conda virtual environment and activate it. shell conda create -n geobev python=3.8 -y conda activate geobev

b. Install PyTorch and torchvision following the official instructions. shell pip install torch==1.10.0+cu111 torchvision==0.11.0+cu111 torchaudio==0.10.0 -f https://download.pytorch.org/whl/torch_stable.html

c. Install GeoBEV as mmdet3d. shell pip install mmcv-full==1.5.3 pip install mmdet==2.27.0 pip install mmsegmentation==0.25.0 pip install -e .

2. Prepare nuScenes dataset as the below folder structure:

GeoBEV ├── data │ ├── nuscenes │ │ ├── lidarseg │ │ ├── maps │ │ ├── samples │ │ ├── samples_instance_mask │ │ ├── samples_point_label │ │ ├── sweeps │ │ ├── v1.0-test | | ├── v1.0-trainval │ │ ├── geobev-nuscenes_infos_train.pkl │ │ ├── geobev-nuscenes_infos_val.pkl a. Download nuScenes 3D detection data HERE and unzip all zip files.

b. The fold samples_instance_mask includes the instance masks of nuScenes images, which are predicted by the HTC model pretrained on nuImages dataset. The prepared data can be downloaded HERE.

c. Create the pkl for GeoBEV by running shell python tools/create_data_bevdet.py

d. Download nuScenes-lidarseg annotations HERE and put it under GeoBEV/data/nuscenes/. Create depth and semantic labels from point cloud by running: shell python tools/generate_point_label.py

3. Train GeoBEV model on nuScenes:

Download the backbones pretrained on nuImages dataset HERE and put them under GeoBEV/ckpts. Then train the GeoBEV model following: shell bash tools/dist_train.sh configs/geobev/geobev-r50-nuimage-cbgs.py 8

4. Evaluate GeoBEV model following:

shell bash tools/dist_test.sh configs/geobev/geobev-r50-nuimage-cbgs.py work_dirs/geobev-r50-nuimage-cbgs/epoch_20_ema.pth 8 --eval bbox

Acknowledgement

This project is not possible without multiple great open-sourced code bases. We list some notable examples below.

Bibtex

If GeoBEV is helpful for your research, please consider citing the following BibTeX entry. @article{zhang2024geobev, title={Geobev: Learning geometric bev representation for multi-view 3d object detection}, author={Zhang, Jinqing and Zhang, Yanan and Qi, Yunlong and Fu, Zehua and Liu, Qingjie and Wang, Yunhong}, journal={arXiv preprint arXiv:2409.01816}, year={2024} }

Owner

  • Login: mengtan00
  • 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

docker/Dockerfile docker
  • nvcr.io/nvidia/tensorrt 22.07-py3 build
docker/serve/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
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 ==1.23.5
  • nuscenes-devkit *
  • plyfile *
  • scikit-image *
  • setuptools ==59.5.0
  • 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 ==0.40.1 test
requirements.txt pypi
setup.py pypi