update docs (open-mmlab#1601)

hjin2902 · Jul 19, 2022 · 7a31b76 · 7a31b76
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diff --git a/docs/en/faq.md b/docs/en/faq.md
@@ -8,31 +8,31 @@ We list some potential troubles encountered by users and developers, along with
 
   The required versions of MMCV, MMDetection and MMSegmentation for different versions of MMDetection3D are as below. Please install the correct version of MMCV, MMDetection and MMSegmentation to avoid installation issues.
 
-  | MMDetection3D version |   MMDetection version   | MMSegmentation version |        MMCV version        |
-  | :-------------------: | :---------------------: | :--------------------: | :------------------------: |
-  |        master         | mmdet>=2.24.0, <=3.0.0  | mmseg>=0.20.0, <=1.0.0 | mmcv-full>=1.4.8, <=1.6.0  |
-  |       v1.0.0rc3       | mmdet>=2.24.0, <=3.0.0  | mmseg>=0.20.0, <=1.0.0 | mmcv-full>=1.4.8, <=1.6.0  |
-  |       v1.0.0rc2       | mmdet>=2.24.0, <=3.0.0  | mmseg>=0.20.0, <=1.0.0 | mmcv-full>=1.4.8, <=1.6.0  |
-  |       v1.0.0rc1       | mmdet>=2.19.0, <=3.0.0  | mmseg>=0.20.0, <=1.0.0 | mmcv-full>=1.4.8, <=1.5.0  |
-  |       v1.0.0rc0       | mmdet>=2.19.0, <=3.0.0  | mmseg>=0.20.0, <=1.0.0 | mmcv-full>=1.3.17, <=1.5.0 |
-  |        0.18.1         | mmdet>=2.19.0, <=3.0.0  | mmseg>=0.20.0, <=1.0.0 | mmcv-full>=1.3.17, <=1.5.0 |
-  |        0.18.0         | mmdet>=2.19.0, <=3.0.0  | mmseg>=0.20.0, <=1.0.0 | mmcv-full>=1.3.17, <=1.5.0 |
-  |        0.17.3         | mmdet>=2.14.0, <=3.0.0  | mmseg>=0.14.1, <=1.0.0 | mmcv-full>=1.3.8, <=1.4.0  |
-  |        0.17.2         | mmdet>=2.14.0, <=3.0.0  | mmseg>=0.14.1, <=1.0.0 | mmcv-full>=1.3.8, <=1.4.0  |
-  |        0.17.1         | mmdet>=2.14.0, <=3.0.0  | mmseg>=0.14.1, <=1.0.0 | mmcv-full>=1.3.8, <=1.4.0  |
-  |        0.17.0         | mmdet>=2.14.0, <=3.0.0  | mmseg>=0.14.1, <=1.0.0 | mmcv-full>=1.3.8, <=1.4.0  |
-  |        0.16.0         | mmdet>=2.14.0, <=3.0.0  | mmseg>=0.14.1, <=1.0.0 | mmcv-full>=1.3.8, <=1.4.0  |
-  |        0.15.0         | mmdet>=2.14.0, <=3.0.0  | mmseg>=0.14.1, <=1.0.0 | mmcv-full>=1.3.8, <=1.4.0  |
-  |        0.14.0         | mmdet>=2.10.0, <=2.11.0 |     mmseg==0.14.0      | mmcv-full>=1.3.1, <=1.4.0  |
-  |        0.13.0         | mmdet>=2.10.0, <=2.11.0 |      Not required      | mmcv-full>=1.2.4, <=1.4.0  |
-  |        0.12.0         | mmdet>=2.5.0, <=2.11.0  |      Not required      | mmcv-full>=1.2.4, <=1.4.0  |
-  |        0.11.0         | mmdet>=2.5.0, <=2.11.0  |      Not required      | mmcv-full>=1.2.4, <=1.3.0  |
-  |        0.10.0         | mmdet>=2.5.0, <=2.11.0  |      Not required      | mmcv-full>=1.2.4, <=1.3.0  |
-  |         0.9.0         | mmdet>=2.5.0, <=2.11.0  |      Not required      | mmcv-full>=1.2.4, <=1.3.0  |
-  |         0.8.0         | mmdet>=2.5.0, <=2.11.0  |      Not required      | mmcv-full>=1.1.5, <=1.3.0  |
-  |         0.7.0         | mmdet>=2.5.0, <=2.11.0  |      Not required      | mmcv-full>=1.1.5, <=1.3.0  |
-  |         0.6.0         | mmdet>=2.4.0, <=2.11.0  |      Not required      | mmcv-full>=1.1.3, <=1.2.0  |
-  |         0.5.0         |          2.3.0          |      Not required      |      mmcv-full==1.0.5      |
+  | MMDetection3D version |   MMDetection version    | MMSegmentation version  |        MMCV version         |
+  | :-------------------: | :----------------------: | :---------------------: | :-------------------------: |
+  |        master         | mmdet>=2.24.0, \<=3.0.0  | mmseg>=0.20.0, \<=1.0.0 | mmcv-full>=1.4.8, \<=1.6.0  |
+  |       v1.0.0rc3       | mmdet>=2.24.0, \<=3.0.0  | mmseg>=0.20.0, \<=1.0.0 | mmcv-full>=1.4.8, \<=1.6.0  |
+  |       v1.0.0rc2       | mmdet>=2.24.0, \<=3.0.0  | mmseg>=0.20.0, \<=1.0.0 | mmcv-full>=1.4.8, \<=1.6.0  |
+  |       v1.0.0rc1       | mmdet>=2.19.0, \<=3.0.0  | mmseg>=0.20.0, \<=1.0.0 | mmcv-full>=1.4.8, \<=1.5.0  |
+  |       v1.0.0rc0       | mmdet>=2.19.0, \<=3.0.0  | mmseg>=0.20.0, \<=1.0.0 | mmcv-full>=1.3.17, \<=1.5.0 |
+  |        0.18.1         | mmdet>=2.19.0, \<=3.0.0  | mmseg>=0.20.0, \<=1.0.0 | mmcv-full>=1.3.17, \<=1.5.0 |
+  |        0.18.0         | mmdet>=2.19.0, \<=3.0.0  | mmseg>=0.20.0, \<=1.0.0 | mmcv-full>=1.3.17, \<=1.5.0 |
+  |        0.17.3         | mmdet>=2.14.0, \<=3.0.0  | mmseg>=0.14.1, \<=1.0.0 | mmcv-full>=1.3.8, \<=1.4.0  |
+  |        0.17.2         | mmdet>=2.14.0, \<=3.0.0  | mmseg>=0.14.1, \<=1.0.0 | mmcv-full>=1.3.8, \<=1.4.0  |
+  |        0.17.1         | mmdet>=2.14.0, \<=3.0.0  | mmseg>=0.14.1, \<=1.0.0 | mmcv-full>=1.3.8, \<=1.4.0  |
+  |        0.17.0         | mmdet>=2.14.0, \<=3.0.0  | mmseg>=0.14.1, \<=1.0.0 | mmcv-full>=1.3.8, \<=1.4.0  |
+  |        0.16.0         | mmdet>=2.14.0, \<=3.0.0  | mmseg>=0.14.1, \<=1.0.0 | mmcv-full>=1.3.8, \<=1.4.0  |
+  |        0.15.0         | mmdet>=2.14.0, \<=3.0.0  | mmseg>=0.14.1, \<=1.0.0 | mmcv-full>=1.3.8, \<=1.4.0  |
+  |        0.14.0         | mmdet>=2.10.0, \<=2.11.0 |      mmseg==0.14.0      | mmcv-full>=1.3.1, \<=1.4.0  |
+  |        0.13.0         | mmdet>=2.10.0, \<=2.11.0 |      Not required       | mmcv-full>=1.2.4, \<=1.4.0  |
+  |        0.12.0         | mmdet>=2.5.0, \<=2.11.0  |      Not required       | mmcv-full>=1.2.4, \<=1.4.0  |
+  |        0.11.0         | mmdet>=2.5.0, \<=2.11.0  |      Not required       | mmcv-full>=1.2.4, \<=1.3.0  |
+  |        0.10.0         | mmdet>=2.5.0, \<=2.11.0  |      Not required       | mmcv-full>=1.2.4, \<=1.3.0  |
+  |         0.9.0         | mmdet>=2.5.0, \<=2.11.0  |      Not required       | mmcv-full>=1.2.4, \<=1.3.0  |
+  |         0.8.0         | mmdet>=2.5.0, \<=2.11.0  |      Not required       | mmcv-full>=1.1.5, \<=1.3.0  |
+  |         0.7.0         | mmdet>=2.5.0, \<=2.11.0  |      Not required       | mmcv-full>=1.1.5, \<=1.3.0  |
+  |         0.6.0         | mmdet>=2.4.0, \<=2.11.0  |      Not required       | mmcv-full>=1.1.3, \<=1.2.0  |
+  |         0.5.0         |          2.3.0           |      Not required       |      mmcv-full==1.0.5       |
 
 - If you faced the error shown below when importing open3d:
 

diff --git a/docs/en/getting_started.md b/docs/en/getting_started.md
@@ -105,13 +105,13 @@ Note:
 1. The git commit id will be written to the version number with step d, e.g. 0.6.0+2e7045c. The version will also be saved in trained models.
 It is recommended that you run step d each time you pull some updates from github. If C++/CUDA codes are modified, then this step is compulsory.
 
-    > Important: Be sure to remove the `./build` folder if you reinstall mmdet with a different CUDA/PyTorch version.
+   > Important: Be sure to remove the `./build` folder if you reinstall mmdet with a different CUDA/PyTorch version.
 
-    ```shell
-    pip uninstall mmdet3d
-    rm -rf ./build
-    find . -name "*.so" | xargs rm
-    ```
+   ```shell
+   pip uninstall mmdet3d
+   rm -rf ./build
+   find . -name "*.so" | xargs rm
+   ```
 
 2. Following the above instructions, MMDetection3D is installed on `dev` mode, any local modifications made to the code will take effect without the need to reinstall it (unless you submit some commits and want to update the version number).
 
@@ -142,7 +142,6 @@ you can install it before installing MMCV.
 
 5. The code can not be built for CPU only environment (where CUDA isn't available) for now.
 
-
 ## Verification
 
 ### Verify with point cloud demo
@@ -206,6 +205,7 @@ More demos about single/multi-modality and indoor/outdoor 3D detection can be fo
 ## Customize Installation
 
 ### CUDA Versions
+
 When installing PyTorch, you need to specify the version of CUDA. If you are not clear on which to choose, follow our recommendations:
 
 - For Ampere-based NVIDIA GPUs, such as GeForce 30 series and NVIDIA A100, CUDA 11 is a must.
@@ -229,8 +229,6 @@ For example, the following command install mmcv-full built for PyTorch 1.10.x an
 pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.10/index.html
 ```
 
-
-
 ### Using MMDetection3D with Docker
 
 We provide a [Dockerfile](https://github.com/open-mmlab/mmdetection3d/blob/master/docker/Dockerfile) to build an image.

diff --git a/docs/en/model_zoo.md b/docs/en/model_zoo.md
@@ -82,28 +82,28 @@ Please refer to [PAConv](https://github.com/open-mmlab/mmdetection3d/blob/master
 
 ### DGCNN
 
-Please refer to [DGCNN](https://github.com/open-mmlab/mmdetection3d/tree/v1.0.0.dev0/configs/dgcnn) for details. We provide DGCNN baselines on S3DIS dataset.
+Please refer to [DGCNN](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/dgcnn) for details. We provide DGCNN baselines on S3DIS dataset.
 
 ### SMOKE
 
-Please refer to [SMOKE](https://github.com/open-mmlab/mmdetection3d/tree/v1.0.0.dev0/configs/smoke) for details. We provide SMOKE baselines on KITTI dataset.
+Please refer to [SMOKE](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/smoke) for details. We provide SMOKE baselines on KITTI dataset.
 
 ### PGD
 
-Please refer to [PGD](https://github.com/open-mmlab/mmdetection3d/tree/v1.0.0.dev0/configs/pgd) for details. We provide PGD baselines on KITTI and nuScenes dataset.
+Please refer to [PGD](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/pgd) for details. We provide PGD baselines on KITTI and nuScenes dataset.
 
 ### PointRCNN
 
-Please refer to [PointRCNN](https://github.com/open-mmlab/mmdetection3d/tree/v1.0.0.dev0/configs/point_rcnn) for details. We provide PointRCNN baselines on KITTI dataset.
+Please refer to [PointRCNN](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/point_rcnn) for details. We provide PointRCNN baselines on KITTI dataset.
 
 ### MonoFlex
 
-Please refer to [MonoFlex](https://github.com/open-mmlab/mmdetection3d/tree/v1.0.0.dev0/configs/monoflex) for details. We provide MonoFlex baselines on KITTI dataset.
+Please refer to [MonoFlex](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/monoflex) for details. We provide MonoFlex baselines on KITTI dataset.
 
 ### SA-SSD
 
 Please refer to [SA-SSD](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/sassd) for details. We provide SA-SSD baselines on the KITTI dataset.
 
 ### Mixed Precision (FP16) Training
 
-Please refer to [Mixed Precision (FP16) Training on PointPillars](https://github.com/open-mmlab/mmdetection3d/tree/v1.0.0.dev0/configs/pointpillars/hv_pointpillars_fpn_sbn-all_fp16_2x8_2x_nus-3d.py) for details.
+Please refer to [Mixed Precision (FP16) Training on PointPillars](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/pointpillars/hv_pointpillars_fpn_sbn-all_fp16_2x8_2x_nus-3d.py) for details.
diff --git a/docs/en/tutorials/backends_support.md b/docs/en/tutorials/backends_support.md
@@ -111,7 +111,7 @@ model = dict(
 
 ```python
 # replace the path with your checkpoint path on Ceph
-load_from = 's3://openmmlab/checkpoints/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_6x8_160e_kitti-3d-car/hv_pointpillars_secfpn_6x8_160e_kitti-3d-car_20200620_230614-77663cd6.pth.pth'
+load_from = 's3://openmmlab/checkpoints/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_6x8_160e_kitti-3d-car/hv_pointpillars_secfpn_6x8_160e_kitti-3d-car_20200620_230614-77663cd6.pth'
 resume_from = None
 workflow = [('train', 1)]
 ```
@@ -149,6 +149,6 @@ You can also delete the local training log after backing up to the specified Cep
 log_config = dict(
     interval=50,
     hooks=[
-        dict(type='TextLoggerHook', out_dir='s3://openmmlab/mmdetection3d'', keep_local=False),
+        dict(type='TextLoggerHook', out_dir='s3://openmmlab/mmdetection3d', keep_local=False),
     ])
 ```
diff --git a/docs/en/tutorials/coord_sys_tutorial.md b/docs/en/tutorials/coord_sys_tutorial.md
@@ -7,6 +7,7 @@ MMDetection3D uses three different coordinate systems. The existence of differen
 Despite the variety of datasets and equipment, by summarizing the line of works on 3D object detection we can roughly categorize coordinate systems into three:
 
 - Camera coordinate system -- the coordinate system of most cameras, in which the positive direction of the y-axis points to the ground, the positive direction of the x-axis points to the right, and the positive direction of the z-axis points to the front.
+
   ```
              up  z front
               |    ^
@@ -22,7 +23,9 @@ Despite the variety of datasets and equipment, by summarizing the line of works
               v
             y down
   ```
+
 - LiDAR coordinate system -- the coordinate system of many LiDARs, in which the negative direction of the z-axis points to the ground, the positive direction of the x-axis points to the front, and the positive direction of the y-axis points to the left.
+
   ```
                z up  x front
                  ^    ^
@@ -32,7 +35,9 @@ Despite the variety of datasets and equipment, by summarizing the line of works
                  |/
   y left <------ 0 ------ right
   ```
+
 - Depth coordinate system -- the coordinate system used by VoteNet, H3DNet, etc., in which the negative direction of the z-axis points to the ground, the positive direction of the x-axis points to the right, and the positive direction of the y-axis points to the front.
+
   ```
             z up  y front
               ^    ^
@@ -215,7 +220,7 @@ See the code [here](https://github.com/open-mmlab/mmdetection3d/blob/master/mmde
 
 #### Q1: Are the box related ops universal to all coordinate system types?
 
-No. For example, [RoI-Aware Pooling ops](https://github.com/open-mmlab/mmcv/blob/master/mmcv/ops/roiaware_pool3d.py) is applicable to boxes under Depth or LiDAR coordinate system only. The evaluation functions for KITTI dataset [here](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/core/evaluation/kitti_utils) are only applicable to boxes under Camera coordinate system since the rotation is clockwise if viewed from above.
+No. For example, [RoI-Aware Pooling ops](https://github.com/open-mmlab/mmcv/blob/master/mmcv/ops/roiaware_pool3d.py) is applicable to boxes under Depth or LiDAR coordinate system only. The evaluation functions for KITTI dataset [here](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/core/evaluation/kitti_utils.py) are only applicable to boxes under Camera coordinate system since the rotation is clockwise if viewed from above.
 
 For each box related op, we have marked the type of boxes to which we can apply the op.
 

diff --git a/docs/zh_cn/model_zoo.md b/docs/zh_cn/model_zoo.md
@@ -78,32 +78,32 @@
 
 ### PAConv
 
-请参考 [PAConv](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/paconv) 获取更多细节，我们在 S3DIS 数据集上给出了相应的结果.
+请参考 [PAConv](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/paconv) 获取更多细节，我们在 S3DIS 数据集上给出了相应的结果。
 
 ### DGCNN
 
-请参考 [DGCNN](https://github.com/open-mmlab/mmdetection3d/tree/v1.0.0.dev0/configs/dgcnn) 获取更多细节，我们在 S3DIS 数据集上给出了相应的结果.
+请参考 [DGCNN](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/dgcnn) 获取更多细节，我们在 S3DIS 数据集上给出了相应的结果。
 
 ### SMOKE
 
-请参考 [SMOKE](https://github.com/open-mmlab/mmdetection3d/tree/v1.0.0.dev0/configs/smoke) 获取更多细节，我们在 KITTI 数据集上给出了相应的结果.
+请参考 [SMOKE](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/smoke) 获取更多细节，我们在 KITTI 数据集上给出了相应的结果。
 
 ### PGD
 
-请参考 [PGD](https://github.com/open-mmlab/mmdetection3d/tree/v1.0.0.dev0/configs/pgd) 获取更多细节，我们在 KITTI 和 nuScenes 数据集上给出了相应的结果.
+请参考 [PGD](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/pgd) 获取更多细节，我们在 KITTI 和 nuScenes 数据集上给出了相应的结果。
 
 ### PointRCNN
 
-请参考 [PointRCNN](https://github.com/open-mmlab/mmdetection3d/tree/v1.0.0.dev0/configs/point_rcnn) 获取更多细节，我们在 KITTI 数据集上给出了相应的结果.
+请参考 [PointRCNN](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/point_rcnn) 获取更多细节，我们在 KITTI 数据集上给出了相应的结果。
 
 ### MonoFlex
 
-请参考 [MonoFlex](https://github.com/open-mmlab/mmdetection3d/tree/v1.0.0.dev0/configs/monoflex) 获取更多细节，我们在 KITTI 数据集上给出了相应的结果.
+请参考 [MonoFlex](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/monoflex) 获取更多细节，我们在 KITTI 数据集上给出了相应的结果。
 
 ### SA-SSD
 
 请参考 [SA-SSD](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/sassd) 获取更多的细节，我们在 KITTI 数据集上给出了相应的基准结果。
 
 ### Mixed Precision (FP16) Training
 
-细节请参考 [Mixed Precision (FP16) Training 在 PointPillars 训练的样例](https://github.com/open-mmlab/mmdetection3d/tree/v1.0.0.dev0/configs/pointpillars/hv_pointpillars_fpn_sbn-all_fp16_2x8_2x_nus-3d.py).
+细节请参考 [Mixed Precision (FP16) Training 在 PointPillars 训练的样例](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/pointpillars/hv_pointpillars_fpn_sbn-all_fp16_2x8_2x_nus-3d.py)。
diff --git a/docs/zh_cn/tutorials/backends_support.md b/docs/zh_cn/tutorials/backends_support.md
@@ -111,7 +111,7 @@ model = dict(
 
 ```python
 # replace the path with your checkpoint path on Ceph
-load_from = 's3://openmmlab/checkpoints/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_6x8_160e_kitti-3d-car/hv_pointpillars_secfpn_6x8_160e_kitti-3d-car_20200620_230614-77663cd6.pth.pth'
+load_from = 's3://openmmlab/checkpoints/mmdetection3d/v0.1.0_models/pointpillars/hv_pointpillars_secfpn_6x8_160e_kitti-3d-car/hv_pointpillars_secfpn_6x8_160e_kitti-3d-car_20200620_230614-77663cd6.pth'
 resume_from = None
 workflow = [('train', 1)]
 ```
@@ -149,6 +149,6 @@ log_config = dict(
 log_config = dict(
     interval=50,
     hooks=[
-        dict(type='TextLoggerHook', out_dir='s3://openmmlab/mmdetection3d'', keep_local=False),
+        dict(type='TextLoggerHook', out_dir='s3://openmmlab/mmdetection3d', keep_local=False),
     ])
 ```
diff --git a/docs/zh_cn/tutorials/coord_sys_tutorial.md b/docs/zh_cn/tutorials/coord_sys_tutorial.md
@@ -7,6 +7,7 @@ MMDetection3D 使用 3 种不同的坐标系。3D 目标检测领域中不同坐
 尽管数据集和采集设备多种多样，但是通过总结 3D 目标检测的工作线，我们可以将坐标系大致分为三类：
 
 - 相机坐标系 -- 大多数相机的坐标系，在该坐标系中 y 轴正方向指向地面，x 轴正方向指向右侧，z 轴正方向指向前方。
+
   ```
               上  z 前
               |    ^
@@ -22,7 +23,9 @@ MMDetection3D 使用 3 种不同的坐标系。3D 目标检测领域中不同坐
               v
             y 下
   ```
+
 - 激光雷达坐标系 -- 众多激光雷达的坐标系，在该坐标系中 z 轴负方向指向地面，x 轴正方向指向前方，y 轴正方向指向左侧。
+
   ```
                 z 上   x 前
                  ^    ^
@@ -32,7 +35,9 @@ MMDetection3D 使用 3 种不同的坐标系。3D 目标检测领域中不同坐
                  |/
   y 左   <------ 0 ------ 右
   ```
+
 - 深度坐标系 -- VoteNet、H3DNet 等模型使用的坐标系，在该坐标系中 z 轴负方向指向地面，x 轴正方向指向右侧，y 轴正方向指向前方。
+
   ```
              z 上   y 前
               ^    ^
@@ -215,7 +220,7 @@ SUN RGB-D 的原始数据不是点云而是 RGB-D 图像。我们通过反投影
 
 #### Q1: 与框相关的算子是否适用于所有坐标系类型？
 
-否。例如，[用于 RoI-Aware Pooling 的算子](https://github.com/open-mmlab/mmcv/blob/master/mmcv/ops/roiaware_pool3d.py)只适用于深度坐标系和激光雷达坐标系下的框。由于如果从上方看，旋转是顺时针的，所以 KITTI 数据集[这里](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/core/evaluation/kitti_utils)的评估函数仅适用于相机坐标系下的框。
+否。例如，[用于 RoI-Aware Pooling 的算子](https://github.com/open-mmlab/mmcv/blob/master/mmcv/ops/roiaware_pool3d.py)只适用于深度坐标系和激光雷达坐标系下的框。由于如果从上方看，旋转是顺时针的，所以 KITTI 数据集[这里](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/core/evaluation/kitti_utils.py)的评估函数仅适用于相机坐标系下的框。
 
 对于每个和框相关的算子，我们注明了其所适用的框类型。