From 8a7f4f052be2bbf7acbc50ea1be075eeacc57491 Mon Sep 17 00:00:00 2001
From: sunjiahao1999 <578431509@qq.com>
Date: Thu, 11 May 2023 20:15:28 +0800
Subject: [PATCH] fix dataset_prepare

---
 docs/en/user_guides/dataset_prepare.md    | 22 ++++++++++++++++++++++
 docs/zh_cn/user_guides/dataset_prepare.md | 12 ++++++++++++
 2 files changed, 34 insertions(+)

diff --git a/docs/en/user_guides/dataset_prepare.md b/docs/en/user_guides/dataset_prepare.md
index 8e4dcca15..6cabcd69c 100644
--- a/docs/en/user_guides/dataset_prepare.md
+++ b/docs/en/user_guides/dataset_prepare.md
@@ -71,6 +71,14 @@ mmdetection3d
 │   │   ├── sunrgbd_data.py
 │   │   ├── sunrgbd_utils.py
 │   │   ├── README.md
+│   ├── semantickitti
+│   │   ├── sequences
+│   │   │   ├── 00
+│   │   │   │   ├── labels
+│   │   │   │   ├── velodyne
+│   │   │   ├── 01
+│   │   │   ├── ..
+│   │   │   ├── 22
 
 ```
 
@@ -177,6 +185,20 @@ python tools/dataset_converters/lyft_data_fixer.py --version v1.01 --root-folder
 
 Note that we follow the original folder names for clear organization. Please rename the raw folders as shown above. Also note that the second command serves the purpose of fixing a corrupted lidar data file. Please refer to the [discussion](https://www.kaggle.com/c/3d-object-detection-for-autonomous-vehicles/discussion/110000) for more details.
 
+### SemanticKITTI
+
+Download SemanticKITTI dataset [HERE](http://semantic-kitti.org/dataset.html#download) and unzip all zip files.
+
+Then generate info files by running:
+
+```bash
+python ./tools/create_data.py semantickitti --root-path ./data/semantickitti --out-dir ./data/semantickitti --extra-tag semantickitti
+```
+
+**Tips**:
+
+- **Ready-made Annotations**. We have also provided SemanticKITTI data annotation files generated offline [here](#summary-of-annotation-files). You could download them and place them under `data/semantickitti/`.
+
 ### S3DIS, ScanNet and SUN RGB-D
 
 To prepare S3DIS data, please see its [README](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/data/s3dis/README.md).
diff --git a/docs/zh_cn/user_guides/dataset_prepare.md b/docs/zh_cn/user_guides/dataset_prepare.md
index 01eab8b32..c553e0012 100644
--- a/docs/zh_cn/user_guides/dataset_prepare.md
+++ b/docs/zh_cn/user_guides/dataset_prepare.md
@@ -174,6 +174,18 @@ python tools/data_converter/lyft_data_fixer.py --version v1.01 --root-folder ./d
 
 注意，为了文件结构的清晰性，我们遵从了 Lyft 数据原先的文件夹名称。请按照上面展示出的文件结构对原始文件夹进行重命名。同样值得注意的是，第二行命令的目的是为了修复一个损坏的激光雷达数据文件。更多细节请参考[该讨论](https://www.kaggle.com/c/3d-object-detection-for-autonomous-vehicles/discussion/110000)。
 
+### SemanticKITTI
+
+在[这里](http://semantic-kitti.org/dataset.html#download)下载 SemanticKITTI 数据集并解压所有文件。通过运行以下指令对 SemanticKITTI 数据进行预处理：
+
+```bash
+python ./tools/create_data.py semantickitti --root-path ./data/semantickitti --out-dir ./data/semantickitti --extra-tag semantickitti
+```
+
+**小贴士**：
+
+- **现成的标注文件**. 我们已经提供了离线处理好的 [SemanticKITTI 标注文件](#数据集标注文件列表)。您直接下载他们并放到 `data/semantickitti` 目录下。
+
 ### S3DIS、ScanNet 和 SUN RGB-D
 
 请参考 S3DIS [README](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/data/s3dis/README.md) 文件以对其进行数据预处理。