[Doc] Add Chinese doc for ScanNet sem seg dataset (#806)

* add scannet seg doc Chinese version

* fix typos

docs/datasets/scannet_sem_seg.md

@@ -2,13 +2,13 @@
 
 ## Dataset preparation
 
-The overall process is similar to ScanNet 3d detection task. Please refer to this [section](https://github.com/open-mmlab/mmdetection3d/blob/master/docs/datasets/scannet_det.md#dataset-preparation). Only a few differences and additional information about the 3d semantic segmentation data will be listed below.
+The overall process is similar to ScanNet 3D detection task. Please refer to this [section](https://github.com/open-mmlab/mmdetection3d/blob/master/docs/datasets/scannet_det.md#dataset-preparation). Only a few differences and additional information about the 3D semantic segmentation data will be listed below.
 
 ### Export ScanNet data
 
-Since ScanNet provides online benchmark for 3d semantic segmentation evaluation on the test set, we need to also download the test scans and put it under `scannet` folder.
+Since ScanNet provides online benchmark for 3D semantic segmentation evaluation on the test set, we need to also download the test scans and put it under `scannet` folder.
 
-The directory structure before data preparation should be as below
+The directory structure before data preparation should be as below:
 
 ```
 mmdetection3d
@@ -28,7 +28,7 @@ mmdetection3d
 │   │   ├── README.md
 ```
 
-Under folder `scans_test` there are 100 test folders in which only raw point cloud data is saved. For instance, under folder `scene0707_00` the files are as below:
+Under folder `scans_test` there are 100 test folders in which only raw point cloud data and its meta file are saved. For instance, under folder `scene0707_00` the files are as below:
 
 - `scene0707_00_vh_clean_2.ply`: Mesh file storing coordinates and colors of each vertex. The mesh's vertices are taken as raw point cloud data.
 - `scene0707_00.txt`: Meta file including sensor parameters, etc. Note: different from data under `scans`, axis-aligned matrix is not provided for test scans.
@@ -37,7 +37,8 @@ Export ScanNet data by running `python batch_load_scannet_data.py`. Note: only p
 
 ### Create dataset
 
-The directory structure after process should be as below
+Similar to the 3D detection task, we create dataset by running `python tools/create_data.py scannet --root-path ./data/scannet --out-dir ./data/scannet --extra-tag scannet`.
+The directory structure after processing should be as below:
 
 ```
 scannet
@@ -67,11 +68,11 @@ scannet
 
 - `seg_info`: The generated infos to support semantic segmentation model training.
     - `train_label_weight.npy`: Weighting factor for each semantic class. Since the number of points in different classes varies greatly, it's a common practice to use label re-weighting to get a better performance.
-    - `train_resampled_scene_idxs.npy`: Re-sampling index for each scene. Different rooms will be sampled multiple times according to their number of points.
+    - `train_resampled_scene_idxs.npy`: Re-sampling index for each scene. Different rooms will be sampled multiple times according to their number of points to balance training data.
 
 ## Training pipeline
 
-A typical training pipeline of ScanNet for 3d semantic segmentation is as below.
+A typical training pipeline of ScanNet for 3D semantic segmentation is as below:
 
 ```python
 train_pipeline = [
@@ -113,12 +114,13 @@ train_pipeline = [
 
 ## Metrics
 
-Typically mean intersection over union (mIoU) is used for evaluation on ScanNet. In detail, we first compute IoU for multiple classes and then average them to get mIoU, please refer to [seg_eval](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/core/evaluation/seg_eval.py).
+Typically mean Intersection over Union (mIoU) is used for evaluation on ScanNet. In detail, we first compute IoU for multiple classes and then average them to get mIoU, please refer to [seg_eval](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/core/evaluation/seg_eval.py).
 
 ## Testing and Making a Submission
 
 By default, our codebase evaluates semantic segmentation results on the validation set.
 If you would like to test the model performance on the online benchmark, add `--format-only` flag in the evaluation script and change `ann_file=data_root + 'scannet_infos_val.pkl'` to `ann_file=data_root + 'scannet_infos_test.pkl'` in the ScanNet dataset's [config](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/_base_/datasets/scannet_seg-3d-20class.py#L126). Remember to specify the `txt_prefix` as the directory to save the testing results.
+
 Taking PointNet++ (SSG) on ScanNet for example, the following command can be used to do inference on test set:
 
 ```

docs_zh-CN/datasets/scannet_sem_seg.md

 # 3D 语义分割 ScanNet 数据集
+
+## 数据集的准备
+
+ScanNet 3D 语义分割数据集的准备和 3D 检测任务的准备很相似，请查看[此文档](https://github.com/open-mmlab/mmdetection3d/blob/master/docs_zh-CN/datasets/scannet_det.md#dataset-preparation)以获取更多细节。
+以下我们只罗列部分 3D 语义分割特有的处理步骤和数据信息。
+
+### 提取 ScanNet 数据
+
+因为 ScanNet 测试集对 3D 语义分割任务提供在线评测的基准，我们也需要下载其测试集并置于 `scannet` 目录下。
+数据预处理前的文件目录结构应如下所示：
+
+
+```
+mmdetection3d
+├── mmdet3d
+├── tools
+├── configs
+├── data
+│   ├── scannet
+│   │   ├── meta_data
+│   │   ├── scans
+│   │   │   ├── scenexxxx_xx
+│   │   ├── scans_test
+│   │   │   ├── scenexxxx_xx
+│   │   ├── batch_load_scannet_data.py
+│   │   ├── load_scannet_data.py
+│   │   ├── scannet_utils.py
+│   │   ├── README.md
+```
+
+在 `scans_test` 目录下有 100 个测试集 scan 的文件夹，每个文件夹仅包含了原始点云数据和基础的数据元文件。
+例如，在 `scene0707_00` 这一目录下的文件如下所示：
+
+- `scene0707_00_vh_clean_2.ply`：原始网格文件，存储有每个顶点的坐标和颜色。网格的顶点会被选取作为处理后点云中的点。
+- `scene0707_00.txt`：数据的元文件，包含数据采集传感器的参数等信息。注意，与 `scans` 目录下的数据 (训练集和验证集) 不同，测试集 scan 并没有提供用于和坐标轴对齐的变换矩阵 (`axis-aligned matrix`)。
+
+用户可以通过运行 `python batch_load_scannet_data.py` 指令来从原始文件中提取 ScanNet 数据。
+注意，测试集只会保存下点云数据，因为没有提供标注信息。
+
+### 创建数据集
+
+与 3D 检测任务类似，我们通过运行 `python tools/create_data.py scannet --root-path ./data/scannet --out-dir ./data/scannet --extra-tag scannet` 指令即可创建 ScanNet 数据集。
+预处理后的数据目录结构如下所示：
+
+```
+scannet
+├── scannet_utils.py
+├── batch_load_scannet_data.py
+├── load_scannet_data.py
+├── scannet_utils.py
+├── README.md
+├── scans
+├── scans_test
+├── scannet_instance_data
+├── points
+│   ├── xxxxx.bin
+├── instance_mask
+│   ├── xxxxx.bin
+├── semantic_mask
+│   ├── xxxxx.bin
+├── seg_info
+│   ├── train_label_weight.npy
+│   ├── train_resampled_scene_idxs.npy
+│   ├── val_label_weight.npy
+│   ├── val_resampled_scene_idxs.npy
+├── scannet_infos_train.pkl
+├── scannet_infos_val.pkl
+├── scannet_infos_test.pkl
+```
+
+- `seg_info`：为支持语义分割任务所生成的信息文件。
+    - `train_label_weight.npy`：每一语义类别的权重系数。因为 ScanNet 中属于不同类的点的数量相差很大，一个常见的操作是在计算损失时对不同类别进行加权 (label re-weighting) 以得到更好的分割性能。
+    - `train_resampled_scene_idxs.npy`：每一个场景 (房间) 的重采样标签。在训练过程中，我们依据每个场景的点的数量，会对其进行不同次数的重采样，以保证训练数据均衡。
+
+## 训练流程
+
+ScanNet 上 3D 语义分割的一种典型数据载入流程如下所示：
+
+```python
+train_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='DEPTH',
+        shift_height=False,
+        use_color=True,
+        load_dim=6,
+        use_dim=[0, 1, 2, 3, 4, 5]),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox_3d=False,
+        with_label_3d=False,
+        with_mask_3d=False,
+        with_seg_3d=True),
+    dict(
+        type='PointSegClassMapping',
+        valid_cat_ids=(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 24, 28,
+                       33, 34, 36, 39),
+        max_cat_id=40),
+    dict(
+        type='IndoorPatchPointSample',
+        num_points=num_points,
+        block_size=1.5,
+        ignore_index=len(class_names),
+        use_normalized_coord=False,
+        enlarge_size=0.2,
+        min_unique_num=None),
+    dict(type='NormalizePointsColor', color_mean=None),
+    dict(type='DefaultFormatBundle3D', class_names=class_names),
+    dict(type='Collect3D', keys=['points', 'pts_semantic_mask'])
+]
+```
+
+- `PointSegClassMapping`：在训练过程中，只有被使用的类别的序号会被映射到类似 [0, 20) 范围内的类别标签。其余的类别序号会被转换为 `ignore_index` 所制定的忽略标签，在本例中是 `20`。
+- `IndoorPatchPointSample`：从输入点云中裁剪一个含有固定数量点的小块 (patch)。`block_size` 指定了裁剪块的边长，在 ScanNet 上这个数值一般设置为 `1.5`。
+- `NormalizePointsColor`：将输入点的颜色信息归一化，通过将 RGB 值除以 `255` 来实现。
+
+## 度量指标
+
+通常我们使用平均交并比 (mean Intersection over Union, mIoU) 作为 ScanNet 语义分割任务的度量指标。
+具体而言，我们先计算所有类别的 IoU，然后取平均值作为 mIoU。
+更多实现细节请参考 [seg_eval.py](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/core/evaluation/seg_eval.py)。
+
+## 在测试集上测试并提交结果
+
+默认情况下，MMDet3D 的代码是在训练集上进行模型训练，然后在验证集上进行模型测试。
+如果你也想在在线基准上测试模型的性能，请在测试命令中加上 `--format-only` 的标记，同时也要将 ScanNet 数据集[配置文件](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/_base_/datasets/scannet_seg-3d-20class.py#L126)中的 `ann_file=data_root + 'scannet_infos_val.pkl'` 改成 `ann_file=data_root + 'scannet_infos_test.pkl'`。
+请记得通过 `txt_prefix` 来指定想要保存测试结果的文件夹名称。
+
+以 PointNet++ (SSG) 在 ScanNet 上的测试为例，你可以运行以下命令来完成测试结果的保存：
+
+```
+./tools/dist_test.sh configs/pointnet2/pointnet2_ssg_16x2_cosine_200e_scannet_seg-3d-20class.py \
+    work_dirs/pointnet2_ssg/latest.pth --format-only \
+    --eval-options txt_prefix=work_dirs/pointnet2_ssg/test_submission
+```
+
+在保存测试结果后，你可以将该文件夹压缩，然后提交到 [ScanNet 在线测试服务器](http://kaldir.vc.in.tum.de/scannet_benchmark/semantic_label_3d)上进行验证。