chore(docs): fix document

projects/BEVFusion/README.md

@@ -34,7 +34,7 @@
 ## Get started
 ### 1. Setup
 
-- Please follow the [installation tutorial](/docs/tutorial/tutorial_detection_3d.md)to set up the environment.
+- Please follow the [installation tutorial](/docs/tutorial/tutorial_detection_3d.md) to set up the environment.
 - Run docker
 
 ```sh

projects/BLIP-2/README.md

@@ -15,7 +15,7 @@
 ## Get started
 ### 1. Setup
 
-- Please follow the [installation tutorial](/docs/tutorial/tutorial_detection_3d.md)to set up the environment.
+- Please follow the [installation tutorial](/docs/tutorial/tutorial_detection_3d.md) to set up the environment.
 - Run docker
 
 ```

projects/CalibrationStatusClassification/README.md

@@ -21,7 +21,7 @@
 ## Get started
 ### 1. Setup
 
-- Please follow the [installation tutorial](/docs/tutorial/tutorial_detection_3d.md)to set up the environment.
+- Please follow the [installation tutorial](/docs/tutorial/tutorial_detection_3d.md) to set up the environment.
 - Docker build for CalibrationStatusClassification
 
 ```sh

projects/CenterPoint/README.md

@@ -2,10 +2,10 @@
 ## Summary
 
 - [Support priority](https://github.com/tier4/AWML/blob/main/docs/design/autoware_ml_design.md#support-priority): Tier S
-- ROS package: [auotoware_lidar_centerpoint] (https://github.com/autowarefoundation/autoware.universe/tree/main/perception/autoware_lidar_centerpoint)
+- ROS package: [auotoware_lidar_centerpoint](https://github.com/autowarefoundation/autoware.universe/tree/main/perception/autoware_lidar_centerpoint)
 - Supported dataset
   - [x] T4dataset
-  - [] NuScenes
+  - [ ] NuScenes
 - Supported model
   - [x] LiDAR-only model
 - Other supported feature
@@ -19,13 +19,23 @@
   - v1 (121m range, grid_size = 760)
     - [CenterPoint base/1.X](./docs/CenterPoint/v1/base.md)
     - [CenterPoint x2/1.X](./docs/CenterPoint/v1/x2.md)
-    - [CenterPoint-ConvNeXtPC base/0.x](./docs/CenterPoint-ConvNeXtPC/v0/base.md)
+  - v2 (121m range, grid_size = 760)
+    - [CenterPoint base/2.X](./docs/CenterPoint/v2/base.md)
+    - [CenterPoint x2/2.X](./docs/CenterPoint/v2/x2.md)
+- CenterPoint-ConvNeXtPC
+  - [CenterPoint-ConvNeXtPC base/0.x](./docs/CenterPoint-ConvNeXtPC/v0/base.md)
+- CenterPoint-ShortRange
+  - v0
+    - [CenterPoint-ShortRange base/0.X](./docs/CenterPoint-ShortRange/v0/base.md)
+  - v2
+    - [CenterPoint-ShortRange base/2.X](./docs/CenterPoint-ShortRange/v2/base.md)
+    - [CenterPoint-ShortRange j6gen2/2.X](./docs/CenterPoint-ShortRange/v2/j6gen2.md)
 
 ## Get started
 ### 1. Setup
 
-- Please follow the [installation tutorial](/docs/tutorial/tutorial_detection_3d.md)to set up the environment.
-- Run docker
+- Please follow the [installation tutorial](/docs/tutorial/tutorial_detection_3d.md) to set up the environment.
+- Run docker.
 
 ```sh
 docker run -it --rm --gpus all --shm-size=64g --name awml -p 6006:6006 -v $PWD/:/workspace -v $PWD/data:/workspace/data autoware-ml
@@ -34,24 +44,24 @@ docker run -it --rm --gpus all --shm-size=64g --name awml -p 6006:6006 -v $PWD/:
 ### 2. Train
 #### 2.1 Environment set up
 
-Set `CUBLAS_WORKSPACE_CONFIG` for the deterministic behavior, plese check this [nvidia doc](https://docs.nvidia.com/cuda/cublas/index.html#results-reproducibility) for more info
+- Set `CUBLAS_WORKSPACE_CONFIG` for the deterministic behavior, plese check this [nvidia doc](https://docs.nvidia.com/cuda/cublas/index.html#results-reproducibility) for more info.
 
 ```sh
 export CUBLAS_WORKSPACE_CONFIG=:4096:8
 ```
 
 #### 2.2. Train CenterPoint model with T4dataset-base
 
-- [choice] Train with a single GPU
-  - Rename config file to use for single GPU and batch size
-  - Change `train_batch_size` and `train_gpu_size` accordingly
+- [choice] Train with a single GPU.
+  - Rename config file to use for single GPU and batch size.
+  - Change `train_batch_size` and `train_gpu_size` accordingly.
 
 ```sh
 # T4dataset (121m)
 python tools/detection3d/train.py projects/CenterPoint/configs/t4dataset/second_secfpn_2xb8_121m_base.py
 ```
 
-- [choice] Train with multi GPU
+- [choice] Train with multi GPU.
 
 ```sh
 # Command
@@ -63,7 +73,7 @@ bash tools/detection3d/dist_script.sh projects/CenterPoint/configs/t4dataset/Cen
 
 ### 3. Evaluation
 
-- Run evaluation on a test set, please select experiment config accordingly
+- Run evaluation on a test set, please select experiment config accordingly.
 
 - [choice] Evaluate with a single GPU
 
@@ -73,8 +83,8 @@ DIR="work_dirs/centerpoint/t4dataset/second_secfpn_2xb8_121m_base/" && \
 python tools/detection3d/test.py projects/CenterPoint/configs/t4dataset/second_secfpn_2xb8_121m_base.py $DIR/epoch_50.pth
 ```
 
-- [choice] Evaluate with multiple GPUs
-  - Note that if you choose to evaluate with multiple GPUs, you might get slightly different results as compared to single GPU due to differences across GPUs
+- [choice] Evaluate with multiple GPUs.
+  - Note that if you choose to evaluate with multiple GPUs, you might get slightly different results as compared to single GPU due to differences across GPUs.
 
 ```sh
 # Command
@@ -88,7 +98,7 @@ bash tools/detection3d/dist_script.sh projects/CenterPoint/configs/t4dataset/Cen
 
 ### 4. Visualization
 
-- Run inference and visualize bounding boxes from a CenterPoint model
+- Run inference and visualize bounding boxes from a CenterPoint model.
 
 ```sh
 # Inference for t4dataset
@@ -100,7 +110,7 @@ where `frame-range` represents the range of frames to visualize.
 
 ### 5. Deploy
 
-- Make an onnx file for a CenterPoint model
+- Make an onnx file for a CenterPoint model.
 
 ```sh
 # Deploy for t4dataset
@@ -111,14 +121,15 @@ python projects/CenterPoint/scripts/deploy.py projects/CenterPoint/configs/t4dat
 where `rot_y_axis_reference` can be removed if we would like to use the original counterclockwise x-axis rotation system.
 
 ## Troubleshooting
+### Difference from original CenterPoint from mmdetection3d v1
 
-- The difference from original CenterPoint from mmdetection3d v1
-  - To maintain the backward compatibility with the previous ML library, we modified the original CenterPoint from mmdetection3d v1 such as:
-    - Exclude voxel center from z-dimension as part of pillar features
-    - Assume that the rotation system in the deployed ONNX file is in clockwise y-axis, and a bounding box is [x, y, z, w, l, h] for the deployed ONNX file
-    - Do not use CBGS dataset to align the experiment configuration with the older library
-- Latest mmdetection3D assumes the lidar coordinate system is in the right-handed x-axis reference, also the dimensionality of a bounding box is [x, y, z, l, w, h], please check [this](https://mmdetection3d.readthedocs.io/en/latest/user_guides/coord_sys_tutorial.html) for more details
+- To maintain the backward compatibility with the previous ML library, we modified the original CenterPoint from mmdetection3d v1 such as:
+  - Exclude voxel center from z-dimension as part of pillar features.
+  - Assume that the rotation system in the deployed ONNX file is in clockwise y-axis, and a bounding box is [x, y, z, w, l, h] for the deployed ONNX file.
+  - Do not use CBGS dataset to align the experiment configuration with the older library.
+- Latest mmdetection3D assumes the lidar coordinate system is in the right-handed x-axis reference, also the dimensionality of a bounding box is [x, y, z, l, w, h], please check [this](https://mmdetection3d.readthedocs.io/en/latest/user_guides/coord_sys_tutorial.html) for more details.
 
 ## Reference
 
+- "Center-based 3D Object Detection and Tracking", Tianwei Yin, Xingyi Zhou, Philipp Krähenbühl, CVPR2021.
 - [CenterPoint of mmdetection3d](https://github.com/open-mmlab/mmdetection3d/tree/main/configs/centerpoint)

projects/FRNet/README.md

@@ -23,7 +23,7 @@
 ## Get started
 ### 1. Setup
 
-- Please follow the [installation tutorial](/docs/tutorial/tutorial_detection_3d.md)to set up the environment.
+- Please follow the [installation tutorial](/docs/tutorial/tutorial_detection_3d.md) to set up the environment.
 - Docker build for FRNet
 
 ```sh

projects/GLIP/README.md

@@ -15,7 +15,7 @@
 ## Get started
 ### 1. Setup
 
-- 1.1. Please follow the [installation tutorial](/docs/tutorial/tutorial_detection_3d.md)to set up the environment.
+- 1.1. Please follow the [installation tutorial](/docs/tutorial/tutorial_detection_3d.md) to set up the environment.
 - 1.2. Download pretrain weight from [GLIP of mmdetection](https://github.com/open-mmlab/mmdetection/tree/main/configs/glip/README.md).
   - In demonstration, you should put `work_dirs/pretrain/glip/`.
 - 1.3. Run docker

projects/StreamPETR/README.md

@@ -20,7 +20,7 @@
 ## Get started
 ### 1. Setup
 
-- Please follow the [installation tutorial](/docs/tutorial/tutorial_detection_3d.md)to set up the environment.
+- Please follow the [installation tutorial](/docs/tutorial/tutorial_detection_3d.md) to set up the environment.
 - Run docker
 
 ```sh

projects/TransFusion/README.md

@@ -29,7 +29,7 @@
 ## Get started
 ### 1. Setup
 
-- Please follow the [installation tutorial](/docs/tutorial/tutorial_detection_3d.md)to set up the environment.
+- Please follow the [installation tutorial](/docs/tutorial/tutorial_detection_3d.md) to set up the environment.
 - Docker build for TransFusion
 
 ```sh

projects/YOLOX_opt/README.md

@@ -24,7 +24,7 @@
 ## Get started
 ### 1. Setup
 
-- Please follow the [installation tutorial](/docs/tutorial/tutorial_detection_3d.md)to set up the environment.
+- Please follow the [installation tutorial](/docs/tutorial/tutorial_detection_3d.md) to set up the environment.
 - Setup libraries necessary for YOLOX deployment with `pip3 install -r ./projects/YOLOX_opt/requirements.txt`
 ### 2. Train
 

tools/analysis_3d/README.md

@@ -19,7 +19,7 @@ They only need to follow `AnalysisCallbackInterface` to add the analyses they ar
 ## Get started
 ### 1. Setup
 
-- Please follow the [installation tutorial](/docs/tutorial/tutorial_detection_3d.md)to set up the environment.
+- Please follow the [installation tutorial](/docs/tutorial/tutorial_detection_3d.md) to set up the environment.
 - Run docker
 
 ```sh

tools/auto_labeling_2d/README.md

@@ -4,7 +4,7 @@
 
 ## 1. Setup environment
 
-- Please follow the [installation tutorial](/docs/tutorial/tutorial_detection_3d.md)to set up the environment.
+- Please follow the [installation tutorial](/docs/tutorial/tutorial_detection_3d.md) to set up the environment.
 
 ## 2. command
 ### (TBD) 2.1 T4dataset to info file

tools/auto_labeling_3d/README.md

@@ -6,7 +6,7 @@ The pipeline of auto labeling for 3D detection.
 
 ## 1. Setup environment
 
-- Please follow the [installation tutorial](/docs/tutorial/tutorial_detection_3d.md)to set up the environment.
+- Please follow the [installation tutorial](/docs/tutorial/tutorial_detection_3d.md) to set up the environment.
 - In addition, please follow the below setting up procedure.
 
 ### Set up environment for auto_labeling_3d

tools/calibration_classification/README.md

@@ -12,7 +12,7 @@ It contains training, evaluation, and visualization for Calibration classificati
 
 ## 1. Setup environment
 
-Please follow the [installation tutorial](/docs/tutorial/tutorial_detection_3d.md)to set up the environment.
+Please follow the [installation tutorial](/docs/tutorial/tutorial_detection_3d.md) to set up the environment.
 
 ## 2. Prepare dataset
 

tools/detection2d/README.md

@@ -11,7 +11,7 @@ Training and evaluation tools for 2D Detection.
 
 ## 1. Set environment
 
-- Please follow the [installation tutorial](/docs/tutorial/tutorial_detection_3d.md)to set up the environment.
+- Please follow the [installation tutorial](/docs/tutorial/tutorial_detection_3d.md) to set up the environment.
 
 ## 2. Prepare dataset
 ### 2.1. COCO dataset