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* fix readme

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* Update release news

* Update README_zh-CN.md

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Co-authored-by: default avatarTai-Wang <tab_wang@outlook.com>
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README.md
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<div>&nbsp;</div>
</div>
[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmdetection3d.readthedocs.io/en/latest/)
[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmdetection3d.readthedocs.io/en/1.1.x/)
[![badge](https://github.com/open-mmlab/mmdetection3d/workflows/build/badge.svg)](https://github.com/open-mmlab/mmdetection3d/actions)
[![codecov](https://codecov.io/gh/open-mmlab/mmdetection3d/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmdetection3d)
[![license](https://img.shields.io/github/license/open-mmlab/mmdetection3d.svg)](https://github.com/open-mmlab/mmdetection3d/blob/master/LICENSE)
**News**: We released the codebase v1.0.0rc3.
**News**:
Note: We are going through large refactoring to provide simpler and more unified usage of many modules.
**v1.1.0rc0** was released in 1/9/2022:
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. In this version, we update some of the model checkpoints after the refactor of coordinate systems. See more details in the [Changelog](docs/en/changelog.md).
- Unifies interfaces of all components based on [MMEngine](https://github.com/open-mmlab/mmengine) and [MMDet 3.x](https://github.com/open-mmlab/mmdetection/tree/3.x).
- A standard data protocol defines and unifies the common keys across different datasets.
- Faster training and testing speed with more strong baselines.
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.
Code and models for the best vision-only method, [FCOS3D](https://arxiv.org/abs/2104.10956), have been released. Please stay tuned for [MoCa](https://arxiv.org/abs/2012.12741).
MMDeploy has supported some MMDetection3d model deployment.
The compatibilities of models are broken due to the unification and simplification of coordinate systems after v1.0.0rc0. For now, most models are benchmarked with similar performance, though few models are still being benchmarked. In the following release, we will update all the model checkpoints and benchmarks. See more details in the [Changelog](docs/en/notes/changelog.md) and [Changelog-v1.0.x](docs/en/notes/changelog_v1.0.x.md).
Documentation: https://mmdetection3d.readthedocs.io/
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English | [简体中文](README_zh-CN.md)
The master branch works with **PyTorch 1.3+**.
The master branch works with **PyTorch 1.6+**.
MMDetection3D is an open source object detection toolbox based on PyTorch, towards the next-generation platform for general 3D detection. It is
a part of the OpenMMLab project developed by [MMLab](http://mmlab.ie.cuhk.edu.hk/).
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- **Support indoor/outdoor 3D detection out of box**
It directly supports popular indoor and outdoor 3D detection datasets, including ScanNet, SUNRGB-D, Waymo, nuScenes, Lyft, and KITTI.
For nuScenes dataset, we also support [nuImages dataset](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/nuimages).
For nuScenes dataset, we also support [nuImages dataset](https://github.com/open-mmlab/mmdetection3d/tree/1.1.x/configs/nuimages).
- **Natural integration with 2D detection**
All the about **300+ models, methods of 40+ papers**, and modules supported in [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/model_zoo.md) can be trained or used in this codebase.
All the about **300+ models, methods of 40+ papers**, and modules supported in [MMDetection](https://github.com/open-mmlab/mmdetection/blob/3.x/docs/en/model_zoo.md) can be trained or used in this codebase.
- **High efficiency**
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## Changelog
v1.0.0rc3 was released in 8/6/2022.
We are excited to announce the release of MMDetection3D 1.1.0rc0.
- Support [SA-SSD](https://openaccess.thecvf.com/content_CVPR_2020/papers/He_Structure_Aware_Single-Stage_3D_Object_Detection_From_Point_Cloud_CVPR_2020_paper.pdf)
MMDet3D 1.1.x unifies the interfaces of dataset, models, evaluation, and visualization with faster training and testing speed.
Please refer to [changelog.md](docs/en/changelog.md) for details and release history.
Please refer to [changelog.md](docs/en/notes/changelog.md) for details and release history.
## Benchmark and model zoo
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## Get Started
Please see [getting_started.md](docs/en/getting_started.md) for the basic usage of MMDetection3D. We provide guidance for quick run [with existing dataset](docs/en/1_exist_data_model.md) and [with customized dataset](docs/en/2_new_data_model.md) for beginners. There are also tutorials for [learning configuration systems](docs/en/tutorials/config.md), [adding new dataset](docs/en/tutorials/customize_dataset.md), [designing data pipeline](docs/en/tutorials/data_pipeline.md), [customizing models](docs/en/tutorials/customize_models.md), [customizing runtime settings](docs/en/tutorials/customize_runtime.md) and [Waymo dataset](docs/en/datasets/waymo_det.md).
Please refer to [FAQ](docs/en/faq.md) for frequently asked questions. When updating the version of MMDetection3D, please also check the [compatibility doc](docs/en/compatibility.md) to be aware of the BC-breaking updates introduced in each version.
## Model deployment
Please see [getting_started.md](docs/en/getting_started.md) for the basic usage of MMDetection3D. We provide guidance for quick run [with existing dataset](docs/en/user_guides/1_exist_data_model.md) and [with customized dataset](docs/en/user_guides/2_new_data_model.md) for beginners. There are also tutorials for [learning configuration systems](docs/en/user_guides/config.md), [adding new dataset](docs/en/advanced_guides/customize_dataset.md), [designing data pipeline](docs/en/user_guides/data_pipeline.md), [customizing models](docs/en/advanced_guides/customize_models.md), [customizing runtime settings](docs/en/advanced_guides/customize_runtime.md) and [Waymo dataset](docs/en/advanced_guides/datasets/waymo_det.md).
Now MMDeploy has supported some MMDetection3D model deployment. Please refer to [model_deployment.md](docs/en/tutorials/model_deployment.md) for more details.
Please refer to [FAQ](docs/en/notes/faq.md) for frequently asked questions. When updating the version of MMDetection3D, please also check the [compatibility doc](docs/en/notes/compatibility.md) to be aware of the BC-breaking updates introduced in each version.
## Citation
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## Projects in OpenMMLab
- [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab foundational library for training deep learning models.
- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
- [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
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README_zh-CN.md
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<div>&nbsp;</div>
</div>
[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmdetection3d.readthedocs.io/en/latest/)
[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmdetection3d.readthedocs.io/en/1.1.x/)
[![badge](https://github.com/open-mmlab/mmdetection3d/workflows/build/badge.svg)](https://github.com/open-mmlab/mmdetection3d/actions)
[![codecov](https://codecov.io/gh/open-mmlab/mmdetection3d/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmdetection3d)
[![license](https://img.shields.io/github/license/open-mmlab/mmdetection3d.svg)](https://github.com/open-mmlab/mmdetection3d/blob/master/LICENSE)
**新闻**: 我们发布了版本 v1.0.0rc3.
**新闻**:
说明：我们正在进行大规模的重构，以提供对许多模块更简单、更统一的使用。
**v1.1.0rc0** 版本已经在 2022.9.1 发布：
由于坐标系的统一和简化，模型的兼容性会受到影响。目前，大多数模型都以类似的性能对齐了精度，但仍有少数模型在进行基准测试。在这个版本中，我们更新了部分坐标系重构后的模型权重文件。您可以在 [变更日志](docs/en/changelog.md) 中查看更多详细信息。
- 基于 [MMEngine](https://github.com/open-mmlab/mmengine) 和 [MMDet 3.x](https://github.com/open-mmlab/mmdetection/tree/3.x) 统一了各组件接口。
- 通过一个标准的数据格式定义和统一了不同数据集的通用内容。
- 实现了更快的训练和测试速度，并提供了更多强大的基准模型。
在第三届 [nuScenes 3D 检测挑战赛](https://www.nuscenes.org/object-detection?externalData=all&mapData=all&modalities=Any)（第五届 AI Driving Olympics, NeurIPS 2020）中，我们获得了最佳 PKL 奖、第三名和最好的纯视觉的结果，相关的代码和模型将会在不久后发布。
最好的纯视觉方法 [FCOS3D](https://arxiv.org/abs/2104.10956) 的代码和模型已经发布。请继续关注我们的多模态检测器 [MoCa](https://arxiv.org/abs/2012.12741)。
MMDeploy 已经支持了部分 MMDetection3D 模型的部署。
由于坐标系的统一和简化，模型的兼容性会受到影响。目前，大多数模型都以类似的性能对齐了精度，但仍有少数模型在进行基准测试。在接下来的版本中，我们将更新所有的模型权重文件和基准。您可以在 [变更日志](docs/en/changelog.md) 和 [v1.0.x版本变更日志](docs/en/notes/changelog_v1.0.x.md) 中查看更多详细信息。
文档: https://mmdetection3d.readthedocs.io/
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[English](README.md) | 简体中文
主分支代码目前支持 PyTorch 1.3 以上的版本。
主分支代码目前支持 PyTorch 1.6 以上的版本。
MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱, 下一代面向3D检测的平台. 它是 OpenMMlab 项目的一部分，这个项目由香港中文大学多媒体实验室和商汤科技联合发起.
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支持室内/室外的3D检测数据集，包括 ScanNet, SUNRGB-D, Waymo, nuScenes, Lyft, KITTI.
对于 nuScenes 数据集, 我们也支持 [nuImages 数据集](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/nuimages).
对于 nuScenes 数据集, 我们也支持 [nuImages 数据集](https://github.com/open-mmlab/mmdetection3d/tree/1.1.x/configs/nuimages).
- **与 2D 检测器的自然整合**
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## 更新日志
最新的版本 v1.0.0rc3 在 2022.6.8 发布。
- 支持 [spconv 2.0](https://github.com/traveller59/spconv)
- 支持基于 [MinkowskiEngine](https://github.com/NVIDIA/MinkowskiEngine) 的 MinkResNet
- 支持在自定义的只有点云的数据集上训练模型
- 更新注册机制以区分不同函数构建的范围
- 用一系列鸟瞰图的算子替换 mmcv.iou3d 以统一旋转框的相关操作
MMDetection3D 1.1.0rc0，MMDet3D 在 OpenMMLab 2.0 项目中的首个版本发布。
如果想了解更多版本更新细节和历史信息，请阅读[更新日志](docs/zh_cn/changelog.md)。
更多细节和版本发布历史可以参考[changelog.md](docs/en/notes/changelog.md).
## 基准测试和模型库
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## 快速入门
请参考[快速入门文档](docs/zh_cn/getting_started.md)学习 MMDetection3D 的基本使用。 我们为新手提供了分别针对[已有数据集](docs/zh_cn/1_exist_data_model.md)和[新数据集](docs/zh_cn/2_new_data_model.md)的使用指南。我们也提供了一些进阶教程，内容覆盖了[学习配置文件](docs/zh_cn/tutorials/config.md), [增加数据集支持](docs/zh_cn/tutorials/customize_dataset.md), [设计新的数据预处理流程](docs/zh_cn/tutorials/data_pipeline.md), [增加自定义模型](docs/zh_cn/tutorials/customize_models.md), [增加自定义的运行时配置](docs/zh_cn/tutorials/customize_runtime.md)和 [Waymo 数据集](docs/zh_cn/tutorials/waymo.md).
请参考 [FAQ](docs/zh_cn/faq.md) 查看一些常见的问题与解答。在升级 MMDetection3D 的版本时，请查看[兼容性文档](docs/zh_cn/compatibility.md)以知晓每个版本引入的不与之前版本兼容的更新。
## 模型部署
请参考[快速入门文档](docs/zh_cn/getting_started.md)学习 MMDetection3D 的基本使用。 我们为新手提供了分别针对[已有数据集](docs/zh_cn/user_guides/1_exist_data_model.md)和[新数据集](docs/zh_cn/user_guides/2_new_data_model.md)的使用指南。我们也提供了一些进阶教程，内容覆盖了[学习配置文件](docs/zh_cn/user_guides/config.md), [增加数据集支持](docs/zh_cn/advanced_guides/customize_dataset.md), [设计新的数据预处理流程](docs/zh_cn/user_guides/data_pipeline.md), [增加自定义模型](docs/zh_cn/advanced_guides/customize_models.md), [增加自定义的运行时配置](docs/zh_cn/advanced_guides/customize_runtime.md)和 [Waymo 数据集](docs/zh_cn/advanced_guides/datasets/waymo.md).
现在 MMDeploy 已经支持了一些 MMDetection3D 模型的部署。请参考 [model_deployment.md](docs/zh_cn/tutorials/model_deployment.md)了解更多细节。
请参考 [FAQ](docs/zh_cn/notes/faq.md) 查看一些常见的问题与解答。在升级 MMDetection3D 的版本时，请查看[兼容性文档](docs/zh_cn/notes/compatibility.md)以知晓每个版本引入的不与之前版本兼容的更新。
## 引用
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## OpenMMLab 的其他项目
- [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab 深度学习模型训练基础库
- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库
- [MIM](https://github.com/open-mmlab/mim): MIM 是 OpenMMlab 项目、算法、模型的统一入口
- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab 图像分类工具箱
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