[Fix] Updata bbox_3d info in cam_instances for mono3d-det task

.circleci/config.yml

@@ -26,6 +26,7 @@ workflows:
  tools/.* lint_only false
  configs/.* lint_only false
  .circleci/.* lint_only false
+ projects/.* lint_only false
  base-revision: dev-1.x
  # this is the path of the configuration we should trigger once
  # path filtering and pipeline parameter value updates are

README.md

@@ -60,7 +60,7 @@ a part of the OpenMMLab project developed by [MMLab](http://mmlab.ie.cuhk.edu.hk
 
 - **High efficiency**
 
- It trains faster than other codebases. The main results are as below. Details can be found in [benchmark.md](./docs/en/benchmarks.md). We compare the number of samples trained per second (the higher, the better). The models that are not supported by other codebases are marked by `×`.
+ It trains faster than other codebases. The main results are as below. Details can be found in [benchmark.md](./docs/en/notes/benchmarks.md). We compare the number of samples trained per second (the higher, the better). The models that are not supported by other codebases are marked by `×`.
 
  | Methods | MMDetection3D | [OpenPCDet](https://github.com/open-mmlab/OpenPCDet) | [votenet](https://github.com/facebookresearch/votenet) | [Det3D](https://github.com/poodarchu/Det3D) |
  | :-----------------: | :-----------: | :--------------------------------------------------: | :----------------------------------------------------: | :-----------------------------------------: |
@@ -165,6 +165,7 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
  <li><a href="configs/votenet">VoteNet (ICCV'2019)</a></li>
  <li><a href="configs/h3dnet">H3DNet (ECCV'2020)</a></li>
  <li><a href="configs/groupfree3d">Group-Free-3D (ICCV'2021)</a></li>
+ <li><a href="configs/fcaf3d">FCAF3D (ECCV'2022)</a></li>
  </ul>
  </td>
  <td>
@@ -202,39 +203,40 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
  </tbody>
 </table>
 
-| | ResNet | ResNeXt | SENet | PointNet++ | DGCNN | HRNet | RegNetX | Res2Net | DLA |
-| ------------- | :----: | :-----: | :---: | :--------: | :---: | :---: | :-----: | :-----: | :-: |
-| SECOND | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ | ✗ |
-| PointPillars | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ | ✗ |
-| FreeAnchor | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ | ✗ |
-| VoteNet | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ |
-| H3DNet | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ |
-| 3DSSD | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ |
-| Part-A2 | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ☐ | ☐ | ✗ |
-| MVXNet | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ☐ | ☐ | ✗ |
-| CenterPoint | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ☐ | ☐ | ✗ |
-| SSN | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ | ✗ |
-| ImVoteNet | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ |
-| FCOS3D | ✓ | ☐ | ☐ | ✗ | ✗ | ☐ | ☐ | ☐ | ✗ |
-| PointNet++ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ |
-| Group-Free-3D | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ |
-| ImVoxelNet | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
-| PAConv | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ |
-| DGCNN | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ |
-| SMOKE | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ |
-| PGD | ✓ | ☐ | ☐ | ✗ | ✗ | ☐ | ☐ | ☐ | ✗ |
-| MonoFlex | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ |
-| SA-SSD | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ☐ | ☐ | ✗ |
-
-**Note:** All the about **300+ models, methods of 40+ papers** in 2D detection supported by [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/model_zoo.md) can be trained or used in this codebase.
+| | ResNet | ResNeXt | SENet | PointNet++ | DGCNN | HRNet | RegNetX | Res2Net | DLA | MinkResNet |
+| ------------- | :----: | :-----: | :---: | :--------: | :---: | :---: | :-----: | :-----: | :-: | :--------: |
+| SECOND | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ | ✗ | ✗ |
+| PointPillars | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ | ✗ | ✗ |
+| FreeAnchor | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ | ✗ | ✗ |
+| VoteNet | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
+| H3DNet | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
+| 3DSSD | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
+| Part-A2 | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ☐ | ☐ | ✗ | ✗ |
+| MVXNet | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ☐ | ☐ | ✗ | ✗ |
+| CenterPoint | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ☐ | ☐ | ✗ | ✗ |
+| SSN | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ✓ | ☐ | ✗ | ✗ |
+| ImVoteNet | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
+| FCOS3D | ✓ | ☐ | ☐ | ✗ | ✗ | ☐ | ☐ | ☐ | ✗ | ✗ |
+| PointNet++ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
+| Group-Free-3D | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
+| ImVoxelNet | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
+| PAConv | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ |
+| DGCNN | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ | ✗ | ✗ | ✗ | ✗ |
+| SMOKE | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ |
+| PGD | ✓ | ☐ | ☐ | ✗ | ✗ | ☐ | ☐ | ☐ | ✗ | ✗ |
+| MonoFlex | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ | ✗ |
+| SA-SSD | ☐ | ☐ | ☐ | ✗ | ✗ | ☐ | ☐ | ☐ | ✗ | ✗ |
+| FCAF3D | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✗ | ✓ |
+
+**Note:** All the about **300+ models, methods of 40+ papers** in 2D detection supported by [MMDetection](https://github.com/open-mmlab/mmdetection/blob/3.x/docs/en/model_zoo.md) can be trained or used in this codebase.
 
 ## Installation
 
 Please refer to [getting_started.md](docs/en/getting_started.md) for installation.
 
 ## Get Started
 
-Please see [getting_started.md](docs/en/getting_started.md) for the basic usage of MMDetection3D. We provide guidance for quick run [with existing dataset](docs/en/user_guides/1_exist_data_model.md) and [with customized dataset](docs/en/user_guides/2_new_data_model.md) for beginners. There are also tutorials for [learning configuration systems](docs/en/user_guides/config.md), [adding new dataset](docs/en/advanced_guides/customize_dataset.md), [designing data pipeline](docs/en/user_guides/data_pipeline.md), [customizing models](docs/en/advanced_guides/customize_models.md), [customizing runtime settings](docs/en/advanced_guides/customize_runtime.md) and [Waymo dataset](docs/en/advanced_guides/datasets/waymo_det.md).
+Please see [getting_started.md](docs/en/getting_started.md) for the basic usage of MMDetection3D. We provide guidance for quick run [with existing dataset](docs/en/user_guides/train_test.md) and [with new dataset](docs/en/user_guides/2_new_data_model.md) for beginners. There are also tutorials for [learning configuration systems](docs/en/user_guides/config.md), [customizing dataset](docs/en/advanced_guides/customize_dataset.md), [designing data pipeline](docs/en/user_guides/data_pipeline.md), [customizing models](docs/en/advanced_guides/customize_models.md), [customizing runtime settings](docs/en/advanced_guides/customize_runtime.md) and [Waymo dataset](docs/en/advanced_guides/datasets/waymo_det.md).
 
 Please refer to [FAQ](docs/en/notes/faq.md) for frequently asked questions. When updating the version of MMDetection3D, please also check the [compatibility doc](docs/en/notes/compatibility.md) to be aware of the BC-breaking updates introduced in each version.
 
@@ -264,11 +266,13 @@ We wish that the toolbox and benchmark could serve the growing research communit
 
 - [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab foundational library for training deep learning models.
 - [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
+- [MMEval](https://github.com/open-mmlab/mmeval): A unified evaluation library for multiple machine learning libraries.
 - [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
 - [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
 - [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
 - [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
 - [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
+- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO series toolbox and benchmark.
 - [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
 - [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.
 - [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.