[Docs] Refactor docs structure (#2429)

* refactor docs structures

* refactor zh_cn docs

* fix docs

* refactor datasets structures

* minor fix

docs/en/advanced_guides/datasets/index.rst

 .. toctree::
    :maxdepth: 3
 
-   kitti_det.md
-   nuscenes_det.md
-   lyft_det.md
-   waymo_det.md
-   sunrgbd_det.md
-   scannet_det.md
-   scannet_sem_seg.md
-   s3dis_sem_seg.md
+   kitti.md
+   nuscenes.md
+   lyft.md
+   waymo.md
+   sunrgbd.md
+   scannet.md
+   s3dis.md

docs/en/advanced_guides/datasets/kitti_det.md → docs/en/advanced_guides/datasets/kitti.md

-# KITTI Dataset for 3D Object Detection
+# KITTI Dataset
 
 This page provides specific tutorials about the usage of MMDetection3D for KITTI dataset.
 

docs/en/advanced_guides/datasets/lyft_det.md → docs/en/advanced_guides/datasets/lyft.md

-# Lyft Dataset for 3D Object Detection
+# Lyft Dataset
 
 This page provides specific tutorials about the usage of MMDetection3D for Lyft dataset.
 

docs/en/advanced_guides/datasets/nuscenes_det.md → docs/en/advanced_guides/datasets/nuscenes.md

-# NuScenes Dataset for 3D Object Detection
+# NuScenes Dataset
 
 This page provides specific tutorials about the usage of MMDetection3D for nuScenes dataset.
 

docs/en/advanced_guides/datasets/s3dis_sem_seg.md → docs/en/advanced_guides/datasets/s3dis.md

-# S3DIS for 3D Semantic Segmentation
+# S3DIS Dataset
 
 ## Dataset preparation
 

docs/en/advanced_guides/datasets/scannet_det.md → docs/en/advanced_guides/datasets/scannet.md

-# ScanNet for 3D Object Detection
+# ScanNet Dataset
+
+MMDetection3D supports LiDAR-based detection and segmentation on ScanNet dataset. This page provides specific tutorials about the usage.
 
 ## Dataset preparation
 
@@ -38,7 +40,7 @@ Under folder `scans` there are overall 1201 train and 312 validation folders in
 - `scene0001_01.txt`: Meta file including axis-aligned matrix, etc.
 - `scene0001_01_vh_clean_2.labels.ply`: Annotation file containing the category of each vertex.
 
-Export ScanNet data by running `python batch_load_scannet_data.py`. The main steps include:
+The procedure of exporting ScanNet data by running `python batch_load_scannet_data.py` mainly includes the following 3 steps:
 
 - Export original files to point cloud, instance label, semantic label and bounding box file.
 - Downsample raw point cloud and filter invalid classes.
@@ -224,6 +226,9 @@ scannet
 - `points/xxxxx.bin`: The `axis-unaligned` point cloud data after downsample. Since ScanNet 3D detection task takes axis-aligned point clouds as input, while ScanNet 3D semantic segmentation task takes unaligned points, we choose to store unaligned points and their axis-align transform matrix. Note: the points would be axis-aligned in pre-processing pipeline [`GlobalAlignment`](https://github.com/open-mmlab/mmdetection3d/blob/9f0b01caf6aefed861ef4c3eb197c09362d26b32/mmdet3d/datasets/pipelines/transforms_3d.py#L423) of 3D detection task.
 - `instance_mask/xxxxx.bin`: The instance label for each point, value range: \[0, NUM_INSTANCES\], 0: unannotated.
 - `semantic_mask/xxxxx.bin`: The semantic label for each point, value range: \[1, 40\], i.e. `nyu40id` standard. Note: the `nyu40id` ID will be mapped to train ID in train pipeline `PointSegClassMapping`.
+- `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 to balance training data.
 - `posed_images/scenexxxx_xx`: The set of `.jpg` images with `.txt` 4x4 poses and the single `.txt` file with camera intrinsic matrix.
 - `scannet_infos_train.pkl`: The train data infos, the detailed info of each scan is as follows:
   - info\['lidar_points'\]: A dict containing all information related to the lidar points.
@@ -285,8 +290,61 @@ train_pipeline = [
   - `RandomFlip3D`: randomly flip the input point cloud horizontally or vertically.
   - `GlobalRotScaleTrans`: rotate the input point cloud, usually in the range of \[-5, 5\] (degrees) for ScanNet; then scale the input point cloud, usually by 1.0 for ScanNet (which means no scaling); finally translate the input point cloud, usually by 0 for ScanNet  (which means no translation).
 
+A typical training pipeline of ScanNet for 3D semantic segmentation is as below:
+
+```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'),
+    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='Pack3DDetInputs', keys=['points', 'pts_semantic_mask'])
+]
+```
+
+- `PointSegClassMapping`: Only the valid category ids will be mapped to class label ids like \[0, 20) during training. Other class ids will be converted to `ignore_index` which equals to `20`.
+- `IndoorPatchPointSample`: Crop a patch containing a fixed number of points from input point cloud. `block_size` indicates the size of the cropped block, typically `1.5` for ScanNet.
+- `NormalizePointsColor`: Normalize the RGB color values of input point cloud by dividing `255`.
+
 ## Metrics
 
-Typically mean Average Precision (mAP) is used for evaluation on ScanNet, e.g. `mAP@0.25` and `mAP@0.5`. In detail, a generic function to compute precision and recall for 3D object detection for multiple classes is called. Please refer to [indoor_eval](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/mmdet3d/evaluation/functional/indoor_eval.py) for more details.
+- **Object Detection**: Typically mean Average Precision (mAP) is used for evaluation on ScanNet, e.g. `mAP@0.25` and `mAP@0.5`. In detail, a generic function to compute precision and recall for 3D object detection for multiple classes is called. Please refer to [indoor_eval](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/mmdet3d/evaluation/functional/indoor_eval.py) for more details.
+
+  **Note**: As introduced in section `Export ScanNet data`, all ground truth 3D bounding box are axis-aligned, i.e. the yaw is zero. So the yaw target of network predicted 3D bounding box is also zero and axis-aligned 3D Non-Maximum Suppression (NMS), which is regardless of rotation, is adopted during post-processing .
+
+- **Semantic Segmentation**: 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/dev-1.x/mmdet3d/evaluation/functional/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:
+
+```
+./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
+```
 
-As introduced in section `Export ScanNet data`, all ground truth 3D bounding box are axis-aligned, i.e. the yaw is zero. So the yaw target of network predicted 3D bounding box is also zero and axis-aligned 3D Non-Maximum Suppression (NMS), which is regardless of rotation, is adopted during post-processing .
+After generating the results, you can basically compress the folder and upload to the [ScanNet evaluation server](http://kaldir.vc.in.tum.de/scannet_benchmark/semantic_label_3d).

docs/en/advanced_guides/datasets/scannet_sem_seg.md

-# ScanNet for 3D Semantic Segmentation
-
-## Dataset preparation
-
-The overall process is similar to ScanNet 3D detection task. Please refer to this [section](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/docs/en/advanced_guides/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.
-
-The directory structure before data preparation should be as below:
-
-```
-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
-```
-
-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.
-
-Export ScanNet data by running `python batch_load_scannet_data.py`. Note: only point cloud data will be saved for test set scans because no annotations are provided.
-
-### Create dataset
-
-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
-├── 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`: 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 to balance training data.
-
-## Training pipeline
-
-A typical training pipeline of ScanNet for 3D semantic segmentation is as below:
-
-```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'),
-    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='Pack3DDetInputs', keys=['points', 'pts_semantic_mask'])
-]
-```
-
-- `PointSegClassMapping`: Only the valid category ids will be mapped to class label ids like \[0, 20) during training. Other class ids will be converted to `ignore_index` which equals to `20`.
-- `IndoorPatchPointSample`: Crop a patch containing a fixed number of points from input point cloud. `block_size` indicates the size of the cropped block, typically `1.5` for ScanNet.
-- `NormalizePointsColor`: Normalize the RGB color values of input point cloud by dividing `255`.
-
-## 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/dev-1.x/mmdet3d/evaluation/functional/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:
-
-```
-./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
-```
-
-After generating the results, you can basically compress the folder and upload to the [ScanNet evaluation server](http://kaldir.vc.in.tum.de/scannet_benchmark/semantic_label_3d).

docs/en/advanced_guides/datasets/sunrgbd_det.md → docs/en/advanced_guides/datasets/sunrgbd.md

-# SUN RGB-D for 3D Object Detection
+# SUN RGB-D Dataset
 
 ## Dataset preparation
 

docs/en/advanced_guides/index.rst

@@ -11,7 +11,7 @@ Supported Tasks
 **************
 
 .. toctree::
-   :maxdepth: 2
+   :maxdepth: 1
 
    supported_tasks/index.rst
 
@@ -20,7 +20,7 @@ Customization
 **************
 
 .. toctree::
-   :maxdepth: 2
+   :maxdepth: 1
 
    customize_dataset.md
    customize_models.md

docs/en/advanced_guides/supported_tasks/index.rst

 .. toctree::
-   :maxdepth: 3
+   :maxdepth: 1
 
    lidar_det3d.md
    vision_det3d.md

docs/en/index.rst

@@ -5,7 +5,6 @@ Welcome to MMDetection3D's documentation!
    :maxdepth: 1
    :caption: Get Started
 
-   overview.md
    get_started.md
 
 .. toctree::

docs/en/user_guides/index.rst

+Train & Test
+**************
 .. toctree::
-   :maxdepth: 3
+   :maxdepth: 1
 
-   2_new_data_model.md
-   backends_support.md
    config.md
    coord_sys_tutorial.md
-   data_pipeline.md
    dataset_prepare.md
-   inference.md
-   index.rst
-   model_deployment.md
+   data_pipeline.md
    train_test.md
+   inference.md
+   new_data_model.md
+
+Useful Tools
+************
+.. toctree::
+   :maxdepth: 1
+
    useful_tools.md
    visualization.md
+   backends_support.md
+   model_deployment.md

docs/en/user_guides/inference.md

@@ -8,7 +8,7 @@ We provide scripts for multi-modality/single-modality (LiDAR-based/vision-based)
 
 ### 3D Detection
 
-#### Single-modality demo
+#### Point cloud demo
 
 To test a 3D detector on point cloud data, simply run:
 
@@ -32,46 +32,46 @@ python demo/pcd_demo.py demo/data/sunrgbd/sunrgbd_000017.bin configs/votenet/vot
 
 Remember to convert the VoteNet checkpoint if you are using mmdetection3d version >= 0.6.0. See its [README](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/votenet/README.md/) for detailed instructions on how to convert the checkpoint.
 
-#### Multi-modality demo
+#### Monocular 3D demo
 
-To test a 3D detector on multi-modality data (typically point cloud and image), simply run:
+To test a monocular 3D detector on image data, simply run:
 
 ```shell
-python demo/multi_modality_demo.py ${PCD_FILE} ${IMAGE_FILE} ${ANNOTATION_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} [--device ${GPU_ID}] [--score-thr ${SCORE_THR}] [--out-dir ${OUT_DIR}] [--show]
+python demo/mono_det_demo.py ${IMAGE_FILE} ${ANNOTATION_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} [--device ${GPU_ID}] [--cam-type ${CAM_TYPE}] [--score-thr ${SCORE-THR}] [--out-dir ${OUT_DIR}] [--show]
 ```
 
-where the `ANNOTATION_FILE` should provide the 3D to 2D projection matrix. The visualization results including a point cloud, an image, predicted 3D bounding boxes and their projection on the image will be saved in `${OUT_DIR}/PCD_NAME`.
+where the `ANNOTATION_FILE` should provide the 3D to 2D projection matrix (camera intrinsic matrix), and `CAM_TYPE` should be specified according to dataset. For example, if you want to inference on the front camera image, the `CAM_TYPE` should be set as `CAM_2` for KITTI, and `CAM_FRONT` for nuScenes. By specifying `CAM_TYPE`, you can even infer on any camera images for datasets with multi-view cameras, such as nuScenes and Waymo. `SCORE-THR` is the 3D bbox threshold while visualization. The visualization results including an image and its predicted 3D bounding boxes projected on the image will be saved in `${OUT_DIR}/IMG_NAME`.
 
-Example on KITTI data using [MVX-Net](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/mvxnet) model:
+Example on nuScenes data using [FCOS3D](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/fcos3d) model:
 
 ```shell
-python demo/multi_modality_demo.py demo/data/kitti/000008.bin demo/data/kitti/000008.png demo/data/kitti/000008.pkl configs/mvxnet/mvx_fpn-dv-second-secfpn_8xb2-80e_kitti-3d-3class.py checkpoints/mvx_fpn-dv-second-secfpn_8xb2-80e_kitti-3d-3class_20200621_003904-10140f2d.pth
+python demo/mono_det_demo.py demo/data/nuscenes/n015-2018-07-24-11-22-45+0800__CAM_BACK__1532402927637525.jpg demo/data/nuscenes/n015-2018-07-24-11-22-45+0800__CAM_BACK__1532402927637525.pkl configs/fcos3d/fcos3d_r101-caffe-dcn-fpn-head-gn_8xb2-1x_nus-mono3d_finetune.py checkpoints/fcos3d_r101-caffe-dcn-fpn-head-gn_8xb2-1x_nus-mono3d_finetune_20210717_095645-8d806dc2.pth
 ```
 
-Example on SUN RGB-D data using [ImVoteNet](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/imvotenet) model:
+Note that when visualizing results of monocular 3D detection for flipped images, the camera intrinsic matrix should also be modified accordingly. See more details and examples in PR [#744](https://github.com/open-mmlab/mmdetection3d/pull/744).
+
+#### Multi-modality demo
+
+To test a 3D detector on multi-modality data (typically point cloud and image), simply run:
 
 ```shell
-python demo/multi_modality_demo.py demo/data/sunrgbd/sunrgbd_000017.bin demo/data/sunrgbd/sunrgbd_000017.jpg demo/data/sunrgbd/sunrgbd_000017_infos.pkl configs/imvotenet/imvotenet_stage2_8xb16_sunrgbd.py checkpoints/imvotenet_stage2_8xb16_sunrgbd_20210323_184021-d44dcb66.pth
+python demo/multi_modality_demo.py ${PCD_FILE} ${IMAGE_FILE} ${ANNOTATION_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} [--device ${GPU_ID}] [--score-thr ${SCORE_THR}] [--out-dir ${OUT_DIR}] [--show]
 ```
 
-### Monocular 3D Detection
+where the `ANNOTATION_FILE` should provide the 3D to 2D projection matrix. The visualization results including a point cloud, an image, predicted 3D bounding boxes and their projection on the image will be saved in `${OUT_DIR}/PCD_NAME`.
 
-To test a monocular 3D detector on image data, simply run:
+Example on KITTI data using [MVX-Net](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/mvxnet) model:
 
 ```shell
-python demo/mono_det_demo.py ${IMAGE_FILE} ${ANNOTATION_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} [--device ${GPU_ID}] [--cam-type ${CAM_TYPE}] [--score-thr ${SCORE-THR}] [--out-dir ${OUT_DIR}] [--show]
+python demo/multi_modality_demo.py demo/data/kitti/000008.bin demo/data/kitti/000008.png demo/data/kitti/000008.pkl configs/mvxnet/mvx_fpn-dv-second-secfpn_8xb2-80e_kitti-3d-3class.py checkpoints/mvx_fpn-dv-second-secfpn_8xb2-80e_kitti-3d-3class_20200621_003904-10140f2d.pth
 ```
 
-where the `ANNOTATION_FILE` should provide the 3D to 2D projection matrix (camera intrinsic matrix), and `CAM_TYPE` should be specified according to dataset. For example, if you want to inference on the front camera image, the `CAM_TYPE` should be set as `CAM_2` for KITTI, and `CAM_FRONT` for nuScenes. By specifying `CAM_TYPE`, you can even infer on any camera images for datasets with multi-view cameras, such as nuScenes and Waymo. `SCORE-THR` is the 3D bbox threshold while visualization. The visualization results including an image and its predicted 3D bounding boxes projected on the image will be saved in `${OUT_DIR}/IMG_NAME`.
-
-Example on nuScenes data using [FCOS3D](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/fcos3d) model:
+Example on SUN RGB-D data using [ImVoteNet](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/imvotenet) model:
 
 ```shell
-python demo/mono_det_demo.py demo/data/nuscenes/n015-2018-07-24-11-22-45+0800__CAM_BACK__1532402927637525.jpg demo/data/nuscenes/n015-2018-07-24-11-22-45+0800__CAM_BACK__1532402927637525.pkl configs/fcos3d/fcos3d_r101-caffe-dcn-fpn-head-gn_8xb2-1x_nus-mono3d_finetune.py checkpoints/fcos3d_r101-caffe-dcn-fpn-head-gn_8xb2-1x_nus-mono3d_finetune_20210717_095645-8d806dc2.pth
+python demo/multi_modality_demo.py demo/data/sunrgbd/sunrgbd_000017.bin demo/data/sunrgbd/sunrgbd_000017.jpg demo/data/sunrgbd/sunrgbd_000017_infos.pkl configs/imvotenet/imvotenet_stage2_8xb16_sunrgbd.py checkpoints/imvotenet_stage2_8xb16_sunrgbd_20210323_184021-d44dcb66.pth
 ```
 
-Note that when visualizing results of monocular 3D detection for flipped images, the camera intrinsic matrix should also be modified accordingly. See more details and examples in PR [#744](https://github.com/open-mmlab/mmdetection3d/pull/744).
-
 ### 3D Segmentation
 
 To test a 3D segmentor on point cloud data, simply run:

docs/en/user_guides/2_new_data_model.md → docs/en/user_guides/new_data_model.md

-# 2: Train with customized datasets
+# Train with Customized Datasets
 
 In this note, you will know how to train and test predefined models with customized datasets. We use the Waymo dataset as an example to describe the whole process.
 

docs/en/user_guides/train_test.md

-# Inference and train with existing models and standard datasets
-
-## Inference with existing models
-
-Here we provide testing scripts to evaluate a whole dataset (SUNRGBD, ScanNet, KITTI, etc.).
-
-For high-level apis easier to integrated into other projects and basic demos, please refer to Verification/Demo under [Get Started](https://mmdetection3d.readthedocs.io/en/dev-1.x/inference.html).
+# Test and Train on Standard Datasets
 
 ### Test existing models on standard datasets
 

docs/en/user_guides/useful_tools.md

-# Useful Tools
-
 We provide lots of useful tools under `tools/` directory.
 
 ## Log Analysis

docs/zh_cn/advanced_guides/datasets/index.rst

 .. toctree::
    :maxdepth: 3
 
-   kitti_det.md
-   nuscenes_det.md
-   lyft_det.md
-   waymo_det.md
-   sunrgbd_det.md
-   scannet_det.md
-   scannet_sem_seg.md
-   s3dis_sem_seg.md
+   kitti.md
+   nuscenes.md
+   lyft.md
+   waymo.md
+   sunrgbd.md
+   scannet.md
+   s3dis.md

docs/zh_cn/advanced_guides/datasets/kitti_det.md → docs/zh_cn/advanced_guides/datasets/kitti.md

-# 3D 目标检测 KITTI 数据集
+# KITTI 数据集
 
 本页提供了有关在 MMDetection3D 中使用 KITTI 数据集的具体教程。
 

docs/zh_cn/advanced_guides/datasets/lyft_det.md → docs/zh_cn/advanced_guides/datasets/lyft.md

-# 3D 目标检测 Lyft 数据集
+# Lyft 数据集
 
 本页提供了有关在 MMDetection3D 中使用 Lyft 数据集的具体教程。