Add notes section

docs/datasets/nuscenes_det.md

@@ -254,3 +254,11 @@ Note that the testing info should be changed to that for testing set instead of
 After generating the `work_dirs/pp-nus/results_eval.json`, you can compress it and submit it to nuScenes benchmark. Please refer to the [nuScenes offical website](https://www.nuscenes.org/object-detection?externalData=all&mapData=all&modalities=Any) for more information.
 
 We can also visualize the prediction results with our developed visualization tools. Please refer to the [visualization doc](https://mmdetection3d.readthedocs.io/en/latest/useful_tools.html#visualization) for more details.
+
+## Notes
+
+### Transformation between `NuScenesBox` and our `CameraInstanceBoxes`.
+
+In general, the main difference of `NuScenesBox` and our `CameraInstanceBoxes` is mainly reflected in the yaw definition. `NuScenesBox` defines the rotation with a quaternion or three Euler angles while ours only defines one yaw angle due to the practical scenario. It requires us to add some additional rotations manually in the pre-processing and post-processing, such as [here](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/datasets/nuscenes_mono_dataset.py#L673).
+
+In addition, please note that the definition of corners and locations are detached in the `NuScenesBox`. For example, in monocular 3D detection, the definition of the box location is in its camera coordinate (see its official [illustration](https://www.nuscenes.org/nuscenes#data-collection) for car setup), which is consistent with [ours](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/core/bbox/structures/cam_box3d.py). In contrast, its corners are defined with the [convention](https://github.com/nutonomy/nuscenes-devkit/blob/02e9200218977193a1058dd7234f935834378319/python-sdk/nuscenes/utils/data_classes.py#L527) "x points forward, y to the left, z up". It results in different philosophy of dimension and rotation definitions from our `CameraInstanceBoxes`. An example to remove similar hacks is PR [#744](https://github.com/open-mmlab/mmdetection3d/pull/744). The same problem also exists in the LiDAR system. To deal with them, we typically add some transformation in the pre-processing and post-processing to guarantee the box will be in our coordinate system during the entire training and inference procedure.