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README.md

@@ -16,7 +16,9 @@ Documentation: https://mmdetection3d.readthedocs.io/
 
 ## Introduction
 
-The master branch works with **PyTorch 1.3 to 1.6**.
+English | [简体中文](README_zh-CN.md)
+
+The master branch works with **PyTorch 1.3+**.
 
 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/).
@@ -36,7 +38,7 @@ a part of the OpenMMLab project developed by [MMLab](http://mmlab.ie.cuhk.edu.hk
 
 - **Natural integration with 2D detection**
 
-  All the about **50+ methods, 300+ models**, and modules supported in [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/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/master/docs/model_zoo.md) can be trained or used in this codebase.
 
 - **High efficiency**
 
@@ -99,9 +101,9 @@ Support methods
 | SSN                | ☐        | ☐        | ☐        | ✗         | ☐     | ✓        | ☐     |
 
 Other features
-- [x] [Dynamic Voxelization](configs/carafe/README.md)
+- [x] [Dynamic Voxelization](configs/dynamic_voxelization/README.md)
 
-**Note:** All the about **300 models, methods of 40+ papers** in 2D detection supported by [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/model_zoo.md) can be trained or used in this codebase.
+**Note:** All the about **300+ models, methods of 40+ papers** in 2D detection supported by [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/model_zoo.md) can be trained or used in this codebase.
 
 ## Installation
 
@@ -109,7 +111,7 @@ Please refer to [getting_started.md](docs/getting_started.md) for installation.
 
 ## Get Started
 
-Please see [getting_started.md](docs/getting_started.md) for the basic usage of MMDetection3D. We provide guidance for quick run [with existing dataset](docs/1_exist_data_model.md) and [with customized dataset](docs/2_new_data_model.md) for beginners. There are also tutorials for [learning configuration systems](docs/tutorials/config.md), [adding new dataset](docs/tutorials/customize_dataset.md), [designing data pipeline](docs/tutorials/data_pipeline.md), [customizing models](docs/tutorials/customize_models.md), [customizing runtime settings](docs/tutorials/customize_runtime.md) and [waymo dataset](docs/tutorials/waymo.md).
+Please see [getting_started.md](docs/getting_started.md) for the basic usage of MMDetection3D. We provide guidance for quick run [with existing dataset](docs/1_exist_data_model.md) and [with customized dataset](docs/2_new_data_model.md) for beginners. There are also tutorials for [learning configuration systems](docs/tutorials/config.md), [adding new dataset](docs/tutorials/customize_dataset.md), [designing data pipeline](docs/tutorials/data_pipeline.md), [customizing models](docs/tutorials/customize_models.md), [customizing runtime settings](docs/tutorials/customize_runtime.md) and [Waymo dataset](docs/tutorials/waymo.md).
 
 ## Contributing
 

README_zh-CN.md

+<div align="center">
+  <img src="resources/mmdet3d-logo.png" width="600"/>
+</div>
+
+[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmdetection3d.readthedocs.io/en/latest/)
+[![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v0.10.0.
+
+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。
+
+文档: https://mmdetection3d.readthedocs.io/
+
+## 简介
+
+[English](README.md) | 简体中文
+
+主分支代码目前支持 PyTorch 1.3 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.
+
+![demo image](resources/mmdet3d_outdoor_demo.gif)
+
+### 主要特性
+
+- **支持多模态/单模态的检测器**
+
+  支持多模态/单模态检测器，包括 MVXNet，VoteNet，PointPillars 等。
+
+- **支持户内/户外的数据集**
+
+  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).
+
+- **与 2D 检测器的自然整合**
+
+   [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/model_zoo.md) 支持的**300+个模型 , 40+的论文算法**, 和相关模块都可以在此代码库中训练或使用。
+
+- **性能高**
+
+   训练速度比其他代码库更快。下表可见主要的对比结果。更多的细节可见[基准测评文档](./docs/benchmarks.md)。我们对比了每秒训练的样本数（值越高越好）。其他代码库不支持的模型被标记为 `×`。
+
+  | Methods | MMDetection3D | [OpenPCDet](https://github.com/open-mmlab/OpenPCDet) |[votenet](https://github.com/facebookresearch/votenet)| [Det3D](https://github.com/poodarchu/Det3D) |
+  |:-------:|:-------------:|:---------:|:-----:|:-----:|
+  | VoteNet | 358           | ×         |   77  | ×     |
+  | PointPillars-car| 141           | ×         |   ×  | 140     |
+  | PointPillars-3class| 107           |44     |   ×      | ×    |
+  | SECOND| 40           |30     |   ×      | ×    |
+  | Part-A2| 17           |14     |   ×      | ×    |
+
+和 [MMDetection](https://github.com/open-mmlab/mmdetection)，[MMCV](https://github.com/open-mmlab/mmcv) 一样, MMDetection3D 也可以作为一个库去支持各式各样的项目.
+
+## 开源许可证
+
+该项目采用 [Apache 2.0 开源许可证](LICENSE)。
+
+## 更新日志
+
+最新的版本 v0.10.0 在 2021.02.01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/changelog.md)。
+
+## 基准测试和模型库
+
+测试结果和模型可以在[模型库](docs/model_zoo.md)中找到。
+
+已支持的骨干网络：
+
+- [x] PointNet (CVPR'2017)
+- [x] PointNet++ (NeurIPS'2017)
+- [x] RegNet (CVPR'2020)
+
+已支持的算法：
+
+- [x] [SECOND (Sensor'2018)](configs/second/README.md)
+- [x] [PointPillars (CVPR'2019)](configs/pointpillars/README.md)
+- [x] [FreeAnchor (NeurIPS'2019)](configs/free_anchor/README.md)
+- [x] [VoteNet (ICCV'2019)](configs/votenet/README.md)
+- [x] [H3DNet (ECCV'2020)](configs/h3dnet/README.md)
+- [x] [3DSSD (CVPR'2020)](configs/3dssd/README.md)
+- [x] [Part-A2 (TPAMI'2020)](configs/parta2/README.md)
+- [x] [MVXNet (ICRA'2019)](configs/mvxnet/README.md)
+- [x] [CenterPoint (Arxiv'2020)](configs/centerpoint/README.md)
+- [x] [SSN (ECCV'2020)](configs/ssn/README.md)
+
+|                    | ResNet   | ResNeXt  | SENet    |PointNet++ | HRNet | RegNetX | Res2Net |
+|--------------------|:--------:|:--------:|:--------:|:---------:|:-----:|:--------:|:-----:|
+| SECOND             | ☐        | ☐        | ☐        | ✗         | ☐     | ✓        | ☐     |
+| PointPillars       | ☐        | ☐        | ☐        | ✗         | ☐     | ✓        | ☐     |
+| FreeAnchor         | ☐        | ☐        | ☐        | ✗         | ☐     | ✓        | ☐     |
+| VoteNet            | ✗        | ✗        | ✗        | ✓         | ✗     | ✗        | ✗     |
+| H3DNet            | ✗        | ✗        | ✗        | ✓         | ✗     | ✗        | ✗     |
+| 3DSSD            | ✗        | ✗        | ✗        | ✓         | ✗     | ✗        | ✗     |
+| Part-A2            | ☐        | ☐        | ☐        | ✗         | ☐     | ✓        | ☐     |
+| MVXNet             | ☐        | ☐        | ☐        | ✗         | ☐     | ✓        | ☐     |
+| CenterPoint        | ☐        | ☐        | ☐        | ✗         | ☐     | ✓        | ☐     |
+| SSN                | ☐        | ☐        | ☐        | ✗         | ☐     | ✓        | ☐     |
+
+其他特性
+- [x] [Dynamic Voxelization](configs/dynamic_voxelization/README.md)
+
+**注意：** [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/model_zoo.md) 支持的基于2D检测的**300+个模型 , 40+的论文算法**在 MMDetection3D 中都可以被训练或使用。
+
+## 安装
+
+请参考[快速入门文档](docs/get_started.md)进行安装。
+
+## 快速入门
+
+请参考[快速入门文档](docs/get_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/1_exist_data_model.md)和[新数据集](docs/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/tutorials/config.md), [增加数据集支持](docs/tutorials/customize_dataset.md), [设计新的数据预处理流程](docs/tutorials/data_pipeline.md), [增加自定义模型](docs/tutorials/customize_models.md), [增加自定义的运行时配置](docs/tutorials/customize_runtime.md)和 [Waymo 数据集](docs/tutorials/waymo.md).
+
+## 贡献指南
+
+我们感谢所有的贡献者为改进和提升 MMDetection3D 所作出的努力。请参考[贡献指南](.github/CONTRIBUTING.md)来了解参与项目贡献的相关指引。
+
+## 致谢
+
+MMDetection3D 是一款由来自不同高校和企业的研发人员共同参与贡献的开源项目。我们感谢所有为项目提供算法复现和新功能支持的贡献者，以及提供宝贵反馈的用户。我们希望这个工具箱和基准测试可以为社区提供灵活的代码工具，供用户复现已有算法并开发自己的新的 3D 检测模型。