We are going through large refactoring to provide simpler and more unified usage of many modules. Thus, few features will be added to the master branch in the following months.
The compatibilities of models are broken due to the unification and simplification of coordinate systems. For now, most models are benchmarked with similar performance, though few models are still being benchmarked.
You can start experiments with v1.0.0.dev0 if you are interested. Please note that our new features will only be supported in v1.0.0 branch afterward.
In the [nuScenes 3D detection challenge](https://www.nuscenes.org/object-detection?externalData=all&mapData=all&modalities=Any) of the 5th AI Driving Olympics in NeurIPS 2020, we obtained the best PKL award and the second runner-up by multi-modality entry, and the best vision-only results.
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@@ -62,9 +68,11 @@ This project is released under the [Apache 2.0 license](LICENSE).
## Changelog
v0.17.0 was released in 1/9/2021.
v0.17.1 was released in 1/10/2021.
Please refer to [changelog.md](docs/changelog.md) for details and release history.
For branch v1.0.0.dev0, please refer to [changelog_v1.0.md](https://github.com/Tai-Wang/mmdetection3d/blob/v1.0.0.dev0-changelog/docs/changelog_v1.0.md) for our latest features and more details.
## Benchmark and model zoo
Supported methods and backbones are shown in the below table.
@@ -10,17 +10,17 @@ It serves as a baseline built on top of mmdetection and mmdetection3d for 3D det
Currently we first support the benchmark on the large-scale nuScenes dataset, which achieved 1st place out of all the vision-only methods in the [nuScenes 3D detecton challenge](https://www.nuscenes.org/object-detection?externalData=all&mapData=all&modalities=Camera) of NeurIPS 2020.
```
@article{wang2021fcos3d,
title={{FCOS3D}: Fully Convolutional One-Stage Monocular 3D Object Detection},
author={Wang, Tai and Zhu, Xinge and Pang, Jiangmiao and Lin, Dahua},
journal={arXiv preprint arXiv:2104.10956},
year={2021}
@inproceedings{wang2021fcos3d,
title={{FCOS3D: Fully} Convolutional One-Stage Monocular 3D Object Detection},
author={Wang, Tai and Zhu, Xinge and Pang, Jiangmiao and Lin, Dahua},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
year={2021}
}
# For the original 2D version
@inproceedings{tian2019fcos,
title = {{FCOS}: Fully Convolutional One-Stage Object Detection},
title = {{FCOS: Fully} Convolutional One-Stage Object Detection},
author = {Tian, Zhi and Shen, Chunhua and Chen, Hao and He, Tong},
- Support a faster but non-deterministic version of hard voxelization
- Completion of dataset tutorials and the Chinese documentation
- Improved the aesthetics of the documentation format
#### Improvements
- Add Chinese Documentation for training on customized datasets and designing customized models (#729, #820)
- Support a faster but non-deterministic version of hard voxelization (#904)
- Update paper titles and code details for metafiles (#917)
- Add a tutorial for KITTI dataset (#953)
- Use Pytorch sphinx theme to improve the format of documentation (#958)
- Use the docker to accelerate CI (#971)
#### Bug Fixes
- Fix the sphinx version used in the documentation (#902)
- Fix a dynamic scatter bug that discards the first voxel by mistake when all input points are valid (#915)
- Fix the inconsistent variable names used in the [unit test](https://github.com/open-mmlab/mmdetection3d/blob/master/tests/test_models/test_voxel_encoder/test_voxel_generator.py) for voxel generater (#919)
- Upgrade to use `build_prior_generator` to replace the legacy `build_anchor_generator` (#941)
- Fix a minor bug caused by a too small difference set in the FreeAnchor Head (#944)
#### Contributors
A total of 8 developers contributed to this release.
