[Docs] Refine the documentation

README.md

@@ -234,7 +234,7 @@ Please refer to [getting_started.md](docs/en/getting_started.md) for installatio
 
 ## 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/user_guides/train_test.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).
+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/train_test.md) and [with new 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), [customizing 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).
 
 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.
 

README_zh-CN.md

@@ -19,7 +19,7 @@
  <div>&nbsp;</div>
 </div>
 
-[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmdetection3d.readthedocs.io/en/1.1/)
+[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmdetection3d.readthedocs.io/zh_CN/1.1/)
 [![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)
@@ -28,7 +28,7 @@
 
 **v1.1.0rc1** 版本已经在 2022.10.11 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/zh_cn/notes/changelog.md)和 [v1.0.x版本变更日志](docs/zh_cn/notes/changelog_v1.0.x.md)中查看更多详细信息。
+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/zh_cn/notes/changelog.md)和 [v1.0.x 版本变更日志](docs/zh_cn/notes/changelog_v1.0.x.md)中查看更多详细信息。
 
 文档：https://mmdetection3d.readthedocs.io/
 
@@ -50,8 +50,7 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱，下一代
 
 - **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。
-
+ 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/1.1/configs/nuimages)。
 
 - **与 2D 检测器的自然整合**
@@ -78,7 +77,7 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱，下一代
 
 ## 更新日志
 
-我们在 2022.10.11 发布了 **1.1.0rc1** 版本.
+我们在 2022.10.11 发布了 **1.1.0rc1** 版本。
 
 更多细节和版本发布历史可以参考 [changelog.md](docs/zh_cn/notes/changelog.md)。
 
@@ -234,7 +233,7 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱，下一代
 
 ## 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/user_guides/train_test.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_det.md)。
+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/train_test.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)，[增加自定义数据集](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_det.md)。
 
 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。
 

docs/en/advanced_guides/customize_dataset.md

@@ -20,35 +20,35 @@ Currently, we only support '.bin' format point cloud for training and inference.
 
 1. Convert pcd to bin: https://github.com/DanielPollithy/pypcd
 
-- You can install pypcd with the following command:
+- You can install `pypcd` with the following command:
 
-```bash
-pip install git+https://github.com/DanielPollithy/pypcd.git
-```
+ ```bash
+ pip install git+https://github.com/DanielPollithy/pypcd.git
+ ```
 
-- You can use the following command to read the pcd file and convert it to bin format and save it:
+- You can use the following script to read the pcd file and convert it to bin format and save it:
 
-```python
-import numpy as np
-from pypcd import pypcd
-
-pcd_data = pypcd.PointCloud.from_path('point_cloud_data.pcd')
-points = np.zeros([pcd_data.width, 4], dtype=np.float32)
-points[:, 0] = pcd_data.pc_data['x'].copy()
-points[:, 1] = pcd_data.pc_data['y'].copy()
-points[:, 2] = pcd_data.pc_data['z'].copy()
-points[:, 3] = pcd_data.pc_data['intensity'].copy().astype(np.float32)
-with open('point_cloud_data.bin', 'wb') as f:
- f.write(points.tobytes())
-```
+ ```python
+ import numpy as np
+ from pypcd import pypcd
+
+ pcd_data = pypcd.PointCloud.from_path('point_cloud_data.pcd')
+ points = np.zeros([pcd_data.width, 4], dtype=np.float32)
+ points[:, 0] = pcd_data.pc_data['x'].copy()
+ points[:, 1] = pcd_data.pc_data['y'].copy()
+ points[:, 2] = pcd_data.pc_data['z'].copy()
+ points[:, 3] = pcd_data.pc_data['intensity'].copy().astype(np.float32)
+ with open('point_cloud_data.bin', 'wb') as f:
+  f.write(points.tobytes())
+ ```
 
 2. Convert las to bin: The common conversion path is las -> pcd -> bin, and the conversion from las -> pcd can be achieved through [this tool](https://github.com/Hitachi-Automotive-And-Industry-Lab/semantic-segmentation-editor).
 
 #### Label Format
 
 The most basic information: 3D bounding box and category label of each scene need to be contained in the annotation `.txt` file. Each line represents a 3D box in a certain scene as follow:
 
-```python
+```
 # format: [x, y, z, dx, dy, dz, yaw, category_name]
 1.23 1.42 0.23 3.96 1.65 1.55 1.56 Car
 3.51 2.15 0.42 1.05 0.87 1.86 1.23 Pedestrian