first commit

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+*.ipynb
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+# cython generated cpp
+data
+.vscode
+.idea
+
+# custom
+*.pkl
+*.pkl.json
+*.log.json
+work_dirs/
+exps/
+*~
+mmdet3d/.mim
+
+# Pytorch
+*.pth
+
+# demo
+*.jpg
+*.png
+data/s3dis/Stanford3dDataset_v1.2_Aligned_Version/
+data/scannet/scans/
+data/sunrgbd/OFFICIAL_SUNRGBD/
+*.obj
+*.ply
+
+# Waymo evaluation
+mmdet3d/core/evaluation/waymo_utils/compute_detection_metrics_main

README.md

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+# Object DGCNN & DETR3D
+
+This repo contains the implementations of Object DGCNN (https://arxiv.org/abs/2110.06923) and DETR3D (https://arxiv.org/abs/2110.06922). Our implementations are built on top of MMdetection3D.  
+
+### Prerequisite
+
+1. mmcv (https://github.com/open-mmlab/mmcv)
+
+2. mmdet (https://github.com/open-mmlab/mmdetection)
+
+3. mmseg (https://github.com/open-mmlab/mmsegmentation)
+
+4. mmdet3d (https://github.com/open-mmlab/mmdetection3d)
+
+### Data
+1. Follow the mmdet3d to process the data.
+
+### Train
+1. Downloads the [pretrained backbone weights](https://drive.google.com/drive/folders/1h5bDg7Oh9hKvkFL-dRhu5-ahrEp2lRNN?usp=sharing) to pretrained/ 
+
+2. For example, to train Object-DGCNN with pillar on 8 GPUs, please use
+
+`tools/dist_train.sh projects/configs/obj_dgcnn/pillar.py 8`
+
+### Evaluation using pretrained models
+1. Download the weights accordingly.  
+
+|  Backbone   | mAP | NDS | Download |
+| :---------: | :----: |:----: | :------: |
+|[DETR3D, ResNet101 w/ DCN](./projects/configs/detr3d/detr3d_res101_gridmask.py)|34.7|42.2|[model](https://drive.google.com/file/d/1YWX-jIS6fxG5_JKUBNVcZtsPtShdjE4O/view?usp=sharing) | [log](https://drive.google.com/file/d/1uvrf42seV4XbWtir-2XjrdGUZ2Qbykid/view?usp=sharing)|
+|[above, + CBGS](./projects/configs/detr3d/detr3d_res101_gridmask_cbgs.py)|34.9|43.4|[model](https://drive.google.com/file/d/1sXPFiA18K9OMh48wkk9dF1MxvBDUCj2t/view?usp=sharing) | [log](https://drive.google.com/file/d/1NJNggvFGqA423usKanqbsZVE_CzF4ltT/view?usp=sharing)|
+|[DETR3D, VoVNet on trainval, evaluation on test set](./projects/configs/detr3d/detr3d_vovnet_gridmask_det_final_trainval_cbgs.py)| 41.2 | 47.9 |[model]() | [log](https://drive.google.com/file/d/1ONEMm_2W9MZAutjQk1UzaqRywz5PMk3p/view?usp=sharing)|
+
+|  Backbone   | mAP | NDS | Download |
+| :---------: | :----: |:----: | :------: |
+|[Object DGCNN, pillar](./projects/configs/obj_dgcnn/pillar.py)|53.2|62.8|[model](https://drive.google.com/file/d/1nd6-PPgdb2b2Bi3W8XPsXPIo2aXn5SO8/view?usp=sharing) | [log](https://drive.google.com/file/d/1A98dWp7SBOdMpo1fHtirwfARvpE38KOn/view?usp=sharing)|
+|[Object DGCNN, voxel](./projects/configs/obj_dgcnn/voxel.py)|58.6|66.0|[model](https://drive.google.com/file/d/1zwUue39W0cAP6lrPxC1Dbq_gqWoSiJUX/view?usp=sharing) | [log](https://drive.google.com/file/d/1pjRMW2ffYdtL_vOYGFcyg4xJImbT7M2p/view?usp=sharing)|
+
+
+2. To test, use  
+`tools/dist_test.sh projects/configs/obj_dgcnn/pillar_cosine.py /path/to/ckpt 8 --eval=bbox`
+
+
+If you find this repo useful for your research, please consider citing the papers
+
+```
+@inproceedings{
+   obj-dgcnn,
+   title={Object DGCNN: 3D Object Detection using Dynamic Graphs},
+   author={Wang, Yue and Solomon, Justin M.},
+   booktitle={2021 Conference on Neural Information Processing Systems ({NeurIPS})},
+   year={2021}
+}
+```
+
+```
+@inproceedings{
+   detr3d,
+   title={DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries},
+   author={Wang, Yue and Guizilini, Vitor and Zhang, Tianyuan and Wang, Yilun and Zhao, Hang and and Solomon, Justin M.},
+   booktitle={The Conference on Robot Learning ({CoRL})},
+   year={2021}
+}
+```