[Fix] Fix demo and visualization (#2453)

* fix demo and visualization * update second checkpoint and link * rename link about scannet config * fix amp config and add lidar_seg in vis_hook * revert main to master in changelog_v1.0.x

[Fix] Fix demo and visualization (#2453)
* fix demo and visualization * update second checkpoint and link * rename link about scannet config * fix amp config and add lidar_seg in vis_hook * revert main to master in changelog_v1.0.x
5ea7fa1b · Jingwei Zhang · GitHub · 1f5dec4c · 5ea7fa1b · 5ea7fa1b
Unverified Commit 5ea7fa1b authored Apr 25, 2023 by Jingwei Zhang Committed by GitHub Apr 25, 2023
20 changed files
--- a/README.md
+++ b/README.md
@@ -82,7 +82,7 @@ a part of the OpenMMLab project developed by [MMLab](http://mmlab.ie.cuhk.edu.hk
 - **Support indoor/outdoor 3D detection out of box**

  It directly supports popular indoor and outdoor 3D detection datasets, including ScanNet, SUNRGB-D, Waymo, nuScenes, Lyft, and KITTI.
-  For nuScenes dataset, we also support [nuImages dataset](https://github.com/open-mmlab/mmdetection3d/tree/latest/configs/nuimages).
+  For nuScenes dataset, we also support [nuImages dataset](https://github.com/open-mmlab/mmdetection3d/tree/main/configs/nuimages).

 - **Natural integration with 2D detection**


--- a/README_zh-CN.md
+++ b/README_zh-CN.md
@@ -78,7 +78,7 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱，下一代
 - **支持户内/户外的数据集**

  支持室内/室外的 3D 检测数据集，包括 ScanNet，SUNRGB-D，Waymo，nuScenes，Lyft，KITTI。
-  对于 nuScenes 数据集，我们也支持 [nuImages 数据集](https://github.com/open-mmlab/mmdetection3d/tree/latest/configs/nuimages)。
+  对于 nuScenes 数据集，我们也支持 [nuImages 数据集](https://github.com/open-mmlab/mmdetection3d/tree/main/configs/nuimages)。

 - **与 2D 检测器的自然整合**


--- a/configs/pointnet2/README.md
+++ b/configs/pointnet2/README.md
@@ -35,7 +35,7 @@ We implement PointNet++ and provide the result and checkpoints on ScanNet and S3

 - Since ScanNet dataset doesn't provide ground-truth labels for the test set, users can only evaluate test set performance by submitting to its online benchmark [website](http://kaldir.vc.in.tum.de/scannet_benchmark/). However, users are only allowed to submit once every two weeks. Therefore, we currently report val set mIoU. Test set performance may be added in the future.

- To generate submission file for ScanNet online benchmark, you need to modify the ScanNet dataset's [config](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/_base_/datasets/scannet_seg-3d-20class.py#L126). Change `ann_file=data_root + 'scannet_infos_val.pkl'` to `ann_file=data_root + 'scannet_infos_test.pkl'`, and then simply run:
+- To generate submission file for ScanNet online benchmark, you need to modify the ScanNet dataset's [config](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/_base_/datasets/scannet-seg.py#L126). Change `ann_file=data_root + 'scannet_infos_val.pkl'` to `ann_file=data_root + 'scannet_infos_test.pkl'`, and then simply run:

  ```shell
  python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} --format-only --options 'txt_prefix=exps/pointnet2_scannet_results'

--- a/configs/second/README.md
+++ b/configs/second/README.md
@@ -22,10 +22,10 @@ We implement SECOND and provide the results and checkpoints on KITTI dataset.

 |                              Backbone                               |  Class  |  Lr schd   | Mem (GB) | Inf time (fps) |  mAP  |                                                                                                                                                                                             Download                                                                                                                                                                                             |
 | :-----------------------------------------------------------------: | :-----: | :--------: | :------: | :------------: | :---: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
-|        [SECFPN](./second_hv_secfpn_8xb6-80e_kitti-3d-car.py)        |   Car   | cyclic 80e |   5.4    |                | 79.07 |               [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/second/hv_second_secfpn_6x8_80e_kitti-3d-car/hv_second_secfpn_6x8_80e_kitti-3d-car_20200620_230238-393f000c.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/second/hv_second_secfpn_6x8_80e_kitti-3d-car/hv_second_secfpn_6x8_80e_kitti-3d-car_20200620_230238.log.json)               |
-|  [SECFPN (FP16)](./hv_second_secfpn_fp16_6x8_80e_kitti-3d-car.py)   |   Car   | cyclic 80e |   2.9    |                | 78.72 |       [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car_20200924_211301-1f5ad833.pth)\| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car_20200924_211301.log.json)        |
-|      [SECFPN](./second_hv_secfpn_8xb6-80e_kitti-3d-3class.py)       | 3 Class | cyclic 80e |   5.4    |                | 65.74 |         [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/second/hv_second_secfpn_6x8_80e_kitti-3d-3class/hv_second_secfpn_6x8_80e_kitti-3d-3class_20210831_022017-ae782e87.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/second/hv_second_secfpn_6x8_80e_kitti-3d-3class/hv_second_secfpn_6x8_80e_kitti-3d-3class_20210831_022017log.json)          |
-| [SECFPN (FP16)](./hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py) | 3 Class | cyclic 80e |   2.9    |                | 67.4  | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class_20200925_110059-05f67bdf.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class_20200925_110059.log.json) |
+|        [SECFPN](./second_hv_secfpn_8xb6-80e_kitti-3d-car.py)        |   Car   | cyclic 80e |   5.4    |                | 78.2  |                       [model](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/second/second_hv_secfpn_8xb6-80e_kitti-3d-car/second_hv_secfpn_8xb6-80e_kitti-3d-car-75d9305e.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/second/second_hv_secfpn_8xb6-80e_kitti-3d-car/second_hv_secfpn_8xb6-80e_kitti-3d-car-20230420_191750.log)                        |
+|  [SECFPN (FP16)](./second_hv_secfpn_8xb6-amp-80e_kitti-3d-car.py)   |   Car   | cyclic 80e |   2.9    |                | 78.72 |       [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car_20200924_211301-1f5ad833.pth)\| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car/hv_second_secfpn_fp16_6x8_80e_kitti-3d-car_20200924_211301.log.json)        |
+|      [SECFPN](./second_hv_secfpn_8xb6-80e_kitti-3d-3class.py)       | 3 Class | cyclic 80e |   5.4    |                | 65.3  |                 [model](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/second/second_hv_secfpn_8xb6-80e_kitti-3d-3class/second_hv_secfpn_8xb6-80e_kitti-3d-3class-b086d0a3.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.1.0_models/second/second_hv_secfpn_8xb6-80e_kitti-3d-3class/second_hv_secfpn_8xb6-80e_kitti-3d-3class-20230420_221130.log)                  |
+| [SECFPN (FP16)](./second_hv_secfpn_8xb6-amp-80e_kitti-3d-3class.py) | 3 Class | cyclic 80e |   2.9    |                | 67.4  | [model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class_20200925_110059-05f67bdf.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fp16/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class_20200925_110059.log.json) |

 ### Waymo

@@ -38,7 +38,7 @@ We implement SECOND and provide the results and checkpoints on KITTI dataset.

 Note:

- See more details about metrics and data split on Waymo [HERE](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/pointpillars). For implementation details, we basically follow the original settings. All of these results are achieved without bells-and-whistles, e.g. ensemble, multi-scale training and test augmentation.
+- See more details about metrics and data split on Waymo [HERE](https://github.com/open-mmlab/mmdetection3d/tree/main/configs/pointpillars). For implementation details, we basically follow the original settings. All of these results are achieved without bells-and-whistles, e.g. ensemble, multi-scale training and test augmentation.
 - `FP16` means Mixed Precision (FP16) is adopted in training.

 ## Citation

--- a/configs/second/metafile.yml
+++ b/configs/second/metafile.yml
@@ -25,8 +25,8 @@ Models:
      - Task: 3D Object Detection
        Dataset: KITTI
        Metrics:
-          mAP: 79.07
-    Weights: https://download.openmmlab.com/mmdetection3d/v0.1.0_models/second/hv_second_secfpn_6x8_80e_kitti-3d-car/hv_second_secfpn_6x8_80e_kitti-3d-car_20200620_230238-393f000c.pth
+          mAP: 78.2
+    Weights: https://download.openmmlab.com/mmdetection3d/v1.1.0_models/second/second_hv_secfpn_8xb6-80e_kitti-3d-car/second_hv_secfpn_8xb6-80e_kitti-3d-car-75d9305e.pth

  - Name: second_hv_secfpn_8xb6-80e_kitti-3d-3class
    In Collection: SECOND
@@ -39,8 +39,8 @@ Models:
      - Task: 3D Object Detection
        Dataset: KITTI
        Metrics:
-          mAP: 65.74
-    Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/second/hv_second_secfpn_6x8_80e_kitti-3d-3class/hv_second_secfpn_6x8_80e_kitti-3d-3class_20210831_022017-ae782e87.pth
+          mAP: 65.3
+    Weights: https://download.openmmlab.com/mmdetection3d/v1.1.0_models/second/second_hv_secfpn_8xb6-80e_kitti-3d-3class/second_hv_secfpn_8xb6-80e_kitti-3d-3class-b086d0a3.pth

  - Name: second_hv_secfpn_sbn-all_16xb2-2x_waymoD5-3d-3class
    In Collection: SECOND

--- a/demo/data/nuscenes/n015-2018-07-24-11-22-45+0800.pkl
+++ b/demo/data/nuscenes/n015-2018-07-24-11-22-45+0800.pkl
--- a/demo/data/nuscenes/n015-2018-07-24-11-22-45+0800__CAM_BACK__1532402927637525_mono3d.pkl
+++ b/demo/data/nuscenes/n015-2018-07-24-11-22-45+0800__CAM_BACK__1532402927637525_mono3d.pkl
--- a/demo/data/nuscenes/n015-2018-07-24-11-22-45+0800__LIDAR_TOP__1532402927647951.pcd.bin
+++ b/demo/data/nuscenes/n015-2018-07-24-11-22-45+0800__LIDAR_TOP__1532402927647951.pcd.bin
--- a/demo/data/sunrgbd/sunrgbd_000017.bin
+++ b/demo/data/sunrgbd/sunrgbd_000017.bin
--- a/demo/data/sunrgbd/sunrgbd_000017.jpg
+++ b/demo/data/sunrgbd/sunrgbd_000017.jpg
--- a/demo/data/sunrgbd/sunrgbd_000017_infos.pkl
+++ b/demo/data/sunrgbd/sunrgbd_000017_infos.pkl
--- a/docs/en/advanced_guides/datasets/scannet.md
+++ b/docs/en/advanced_guides/datasets/scannet.md
@@ -337,12 +337,12 @@ train_pipeline = [
 ## 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.
+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/main/configs/_base_/datasets/scannet-seg.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 \
+./tools/dist_test.sh configs/pointnet2/pointnet2_ssg_16x2_cosine_200e_scannet-seg.py \
    work_dirs/pointnet2_ssg/latest.pth --format-only \
    --eval-options txt_prefix=work_dirs/pointnet2_ssg/test_submission
 ```

--- a/docs/en/advanced_guides/supported_tasks/lidar_sem_seg3d.md
+++ b/docs/en/advanced_guides/supported_tasks/lidar_sem_seg3d.md
@@ -74,14 +74,14 @@ The evaluation results will be printed in the command like:
 In addition, you can also evaluate a specific model checkpoint after training is finished. Simply run scripts like the following:

 ```
-./tools/dist_test.sh configs/pointnet2/pointnet2_ssg_16x2_cosine_200e_scannet_seg-3d-20class.py work_dirs/pointnet2_ssg/latest.pth 8
+./tools/dist_test.sh configs/pointnet2/pointnet2_ssg_16x2_cosine_200e_scannet-seg.py work_dirs/pointnet2_ssg/latest.pth 8
 ```

 ## Testing and Making a Submission

 If you would like to only conduct inference or test the model performance on the online benchmark,
 you should change `ann_file='scannet_infos_val.pkl'` to `ann_file='scannet_infos_test.pkl'` in the
-ScanNet dataset's [config](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/configs/_base_/datasets/scannet_seg-3d-20class.py#L129). Remember to
+ScanNet dataset's [config](https://github.com/open-mmlab/mmdetection3d/blob/dev-1.x/configs/_base_/datasets/scannet-seg.py#L129). Remember to
 specify the `submission_prefix` in the `test_evaluator`,
 e.g., adding `test_evaluator = dict(type='SegMetric', submission_prefix=work_dirs/pointnet2_ssg/test_submission`) or just add `--cfg-options test_evaluator.submission_prefix=work_dirs/pointnet2_ssg/test_submission` in the end of command.
 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).

--- a/docs/en/model_zoo.md
+++ b/docs/en/model_zoo.md
@@ -10,75 +10,75 @@

 ### SECOND

-Please refer to [SECOND](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/second) for details. We provide SECOND baselines on KITTI and Waymo datasets.
+Please refer to [SECOND](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/second) for details. We provide SECOND baselines on KITTI and Waymo datasets.

 ### PointPillars

-Please refer to [PointPillars](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/pointpillars) for details. We provide pointpillars baselines on KITTI, nuScenes, Lyft, and Waymo datasets.
+Please refer to [PointPillars](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/pointpillars) for details. We provide pointpillars baselines on KITTI, nuScenes, Lyft, and Waymo datasets.

 ### Part-A2

-Please refer to [Part-A2](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/parta2) for details.
+Please refer to [Part-A2](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/parta2) for details.

 ### VoteNet

-Please refer to [VoteNet](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/votenet) for details. We provide VoteNet baselines on ScanNet and SUNRGBD datasets.
+Please refer to [VoteNet](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/votenet) for details. We provide VoteNet baselines on ScanNet and SUNRGBD datasets.

 ### Dynamic Voxelization

-Please refer to [Dynamic Voxelization](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/dynamic_voxelization) for details.
+Please refer to [Dynamic Voxelization](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/dynamic_voxelization) for details.

 ### MVXNet

-Please refer to [MVXNet](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/mvxnet) for details.
+Please refer to [MVXNet](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/mvxnet) for details.

 ### RegNetX

-Please refer to [RegNet](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/regnet) for details. We provide pointpillars baselines with RegNetX backbones on nuScenes and Lyft datasets currently.
+Please refer to [RegNet](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/regnet) for details. We provide pointpillars baselines with RegNetX backbones on nuScenes and Lyft datasets currently.

 ### nuImages

-We also support baseline models on [nuImages dataset](https://www.nuscenes.org/nuimages). Please refer to [nuImages](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/nuimages) for details. We report Mask R-CNN, Cascade Mask R-CNN and HTC results currently.
+We also support baseline models on [nuImages dataset](https://www.nuscenes.org/nuimages). Please refer to [nuImages](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/nuimages) for details. We report Mask R-CNN, Cascade Mask R-CNN and HTC results currently.

 ### H3DNet

-Please refer to [H3DNet](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/h3dnet) for details.
+Please refer to [H3DNet](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/h3dnet) for details.

 ### 3DSSD

-Please refer to [3DSSD](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/3dssd) for details.
+Please refer to [3DSSD](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/3dssd) for details.

 ### CenterPoint

-Please refer to [CenterPoint](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/centerpoint) for details.
+Please refer to [CenterPoint](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/centerpoint) for details.

 ### SSN

-Please refer to [SSN](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/ssn) for details. We provide pointpillars with shape-aware grouping heads used in SSN on the nuScenes and Lyft datasets currently.
+Please refer to [SSN](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/ssn) for details. We provide pointpillars with shape-aware grouping heads used in SSN on the nuScenes and Lyft datasets currently.

 ### ImVoteNet

-Please refer to [ImVoteNet](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/imvotenet) for details. We provide ImVoteNet baselines on SUNRGBD dataset.
+Please refer to [ImVoteNet](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/imvotenet) for details. We provide ImVoteNet baselines on SUNRGBD dataset.

 ### FCOS3D

-Please refer to [FCOS3D](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/fcos3d) for details. We provide FCOS3D baselines on the nuScenes dataset.
+Please refer to [FCOS3D](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/fcos3d) for details. We provide FCOS3D baselines on the nuScenes dataset.

 ### PointNet++

-Please refer to [PointNet++](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/pointnet2) for details. We provide PointNet++ baselines on ScanNet and S3DIS datasets.
+Please refer to [PointNet++](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/pointnet2) for details. We provide PointNet++ baselines on ScanNet and S3DIS datasets.

 ### Group-Free-3D

-Please refer to [Group-Free-3D](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/groupfree3d) for details. We provide Group-Free-3D baselines on ScanNet dataset.
+Please refer to [Group-Free-3D](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/groupfree3d) for details. We provide Group-Free-3D baselines on ScanNet dataset.

 ### ImVoxelNet

-Please refer to [ImVoxelNet](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/imvoxelnet) for details. We provide ImVoxelNet baselines on KITTI dataset.
+Please refer to [ImVoxelNet](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/imvoxelnet) for details. We provide ImVoxelNet baselines on KITTI dataset.

 ### PAConv

-Please refer to [PAConv](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/paconv) for details. We provide PAConv baselines on S3DIS dataset.
+Please refer to [PAConv](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/paconv) for details. We provide PAConv baselines on S3DIS dataset.

 ### DGCNN

@@ -102,11 +102,11 @@ Please refer to [MonoFlex](https://github.com/open-mmlab/mmdetection3d/tree/v1.0

 ### 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.
+Please refer to [SA-SSD](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/sassd) for details. We provide SA-SSD baselines on the KITTI dataset.

 ### FCAF3D

-Please refer to [FCAF3D](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/fcaf3d) for details. We provide FCAF3D baselines on the ScanNet, S3DIS, and SUN RGB-D datasets.
+Please refer to [FCAF3D](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/fcaf3d) for details. We provide FCAF3D baselines on the ScanNet, S3DIS, and SUN RGB-D datasets.

 ### PV-RCNN


--- a/docs/en/notes/benchmarks.md
+++ b/docs/en/notes/benchmarks.md
@@ -26,7 +26,7 @@ We compare the training speed (samples/s) with other codebases if they implement

 ### Modification for Calculating Speed

- __MMDetection3D__: We try to use as similar settings as those of other codebases as possible using [benchmark configs](https://github.com/open-mmlab/MMDetection3D/blob/master/configs/benchmark).
+- __MMDetection3D__: We try to use as similar settings as those of other codebases as possible using [benchmark configs](https://github.com/open-mmlab/MMDetection3D/blob/main/configs/benchmark).

 - __Det3D__: For comparison with Det3D, we use the commit [519251e](https://github.com/poodarchu/Det3D/tree/519251e72a5c1fdd58972eabeac67808676b9bb7).


--- a/docs/en/user_guides/inference.md
+++ b/docs/en/user_guides/inference.md
@@ -18,20 +18,18 @@ python demo/pcd_demo.py ${PCD_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} [--device

 The visualization results including a point cloud and predicted 3D bounding boxes will be saved in `${OUT_DIR}/PCD_NAME`, which you can open using [MeshLab](http://www.meshlab.net/). Note that if you set the flag `--show`, the prediction result will be displayed online using [Open3D](http://www.open3d.org/).

-Example on KITTI data using [SECOND](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/second) model:
+Example on KITTI data using [PointPillars model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pointpillars/hv_pointpillars_secfpn_6x8_160e_kitti-3d-car/hv_pointpillars_secfpn_6x8_160e_kitti-3d-car_20220331_134606-d42d15ed.pth):

 ```shell
-python demo/pcd_demo.py demo/data/kitti/000008.bin configs/second/second_hv-secfpn_8xb6-80e_kitti-3d-car.py checkpoints/second_hv-secfpn_8xb6-80e_kitti-3d-car_20200620_230238-393f000c.pth
+python demo/pcd_demo.py demo/data/kitti/000008.bin configs/pointpillars/pointpillars_hv_secfpn_8xb6-160e_kitti-3d-car.py ${CHECKPOINT_FILE} --show
 ```

-Example on SUN RGB-D data using [VoteNet](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/votenet) model:
+Example on SUN RGB-D data using [VoteNet model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/votenet/votenet_16x8_sunrgbd-3d-10class/votenet_16x8_sunrgbd-3d-10class_20210820_162823-bf11f014.pth):

 ```shell
-python demo/pcd_demo.py demo/data/sunrgbd/sunrgbd_000017.bin configs/votenet/votenet_8xb16_sunrgbd-3d.py checkpoints/votenet_8xb16_sunrgbd-3d_20200620_230238-4483c0c0.pth
+python demo/pcd_demo.py demo/data/sunrgbd/sunrgbd_000017.bin configs/votenet/votenet_8xb16_sunrgbd-3d.py ${CHECKPOINT_FILE} --show
 ```

-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.
-
 #### Monocular 3D demo

 To test a monocular 3D detector on image data, simply run:
@@ -42,13 +40,21 @@ python demo/mono_det_demo.py ${IMAGE_FILE} ${ANNOTATION_FILE} ${CONFIG_FILE} ${C

 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 KITTI data using [PGD model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/pgd/pgd_r101_caffe_fpn_gn-head_3x4_4x_kitti-mono3d/pgd_r101_caffe_fpn_gn-head_3x4_4x_kitti-mono3d_20211022_102608-8a97533b.pth):
+
+```shell
+python demo/mono_det_demo.py demo/data/kitti/000008.png demo/data/kitti/000008.pkl  configs/pgd/pgd_r101-caffe_fpn_head-gn_4xb3-4x_kitti-mono3d.py ${CHECKPOINT_FILE}  --show --cam-type CAM2 --score-thr 8
+```
+
+**Note**: For PGD, the prediction score is not among (0, 1).
+
+Example on nuScenes data using [FCOS3D model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/fcos3d/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune/fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d_finetune_20210717_095645-8d806dc2.pth):

 ```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/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.pkl  configs/fcos3d/fcos3d_r101-caffe-dcn_fpn_head-gn_8xb2-1x_nus-mono3d_finetune.py ${CHECKPOINT_FILE}  --show --cam-type CAM_BACK
 ```

-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).
+**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

@@ -60,16 +66,16 @@ python demo/multi_modality_demo.py ${PCD_FILE} ${IMAGE_FILE} ${ANNOTATION_FILE}

 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`.

-Example on KITTI data using [MVX-Net](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/mvxnet) model:
+Example on KITTI data using [MVX-Net](https://github.com/open-mmlab/mmdetection3d/tree/main/configs/mvxnet) 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/multi_modality_demo.py demo/data/kitti/000008.bin demo/data/kitti/000008.png demo/data/kitti/000008.pkl configs/mvxnet/mvxnet_fpn_dv_second_secfpn_8xb2-80e_kitti-3d-3class.py ${CHECKPOINT_FILE} --cam-type CAM2 --show
 ```

-Example on SUN RGB-D data using [ImVoteNet](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/imvotenet) model:
+Example on SUN RGB-D data using [ImVoteNet model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/imvotenet/imvotenet_stage2_16x8_sunrgbd-3d-10class/imvotenet_stage2_16x8_sunrgbd-3d-10class_20210819_192851-1bcd1b97.pth):

 ```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 demo/data/sunrgbd/000017.bin demo/data/sunrgbd/000017.jpg demo/data/sunrgbd/sunrgbd_000017_infos.pkl configs/imvotenet/imvotenet_stage2_8xb16_sunrgbd-3d.py ${CHECKPOINT_FILE} --cam-type CAM0 --show --score-thr 0.6
 ```

 ### 3D Segmentation
@@ -82,8 +88,8 @@ python demo/pcd_seg_demo.py ${PCD_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} [--dev

 The visualization results including a point cloud and its predicted 3D segmentation mask will be saved in `${OUT_DIR}/PCD_NAME`.

-Example on ScanNet data using [PointNet++ (SSG)](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/pointnet2) model:
+Example on ScanNet data using [PointNet++ (SSG) model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/pointnet2/pointnet2_ssg_16x2_cosine_200e_scannet_seg-3d-20class/pointnet2_ssg_16x2_cosine_200e_scannet_seg-3d-20class_20210514_143644-ee73704a.pth):

 ```shell
-python demo/pc_seg_demo.py demo/data/scannet/scene0000_00.bin configs/pointnet2/pointnet2_ssg_2xb16-cosine-200e_scannet-seg.py checkpoints/pointnet2_ssg_2xb16-cosine-200e_scannet-seg_20210514_143644-ee73704a.pth
+python demo/pcd_seg_demo.py demo/data/scannet/scene0000_00.bin configs/pointnet2/pointnet2_ssg_2xb16-cosine-200e_scannet-seg.py ${CHECKPOINT_FILE} --show
 ```
--- a/docs/en/user_guides/useful_tools.md
+++ b/docs/en/user_guides/useful_tools.md
@@ -202,7 +202,7 @@ python -u tools/dataset_converters/nuimage_converter.py --data-root ${DATA_ROOT}
 - `--nproc`: number of workers for data preparation, defaults to `4`. Larger number could reduce the preparation time as images are processed in parallel.
 - `--extra-tag`: extra tag of the annotations, defaults to `nuimages`. This can be used to separate different annotations processed in different time for study.

-More details could be referred to the [doc](https://mmdetection3d.readthedocs.io/en/latest/data_preparation.html) for dataset preparation and [README](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/nuimages/README.md/) for nuImages dataset.
+More details could be referred to the [doc](https://mmdetection3d.readthedocs.io/en/latest/data_preparation.html) for dataset preparation and [README](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/nuimages/README.md/) for nuImages dataset.

 &#8195;


--- a/docs/en/user_guides/visualization.md
+++ b/docs/en/user_guides/visualization.md
@@ -190,7 +190,7 @@ python tools/misc/browse_dataset.py configs/mvxnet/dv_mvx-fpn_second_secfpn_adam
 You can simply browse different datasets using different configs, e.g. visualizing the ScanNet dataset in 3D semantic segmentation task:

 ```shell
-python tools/misc/browse_dataset.py configs/_base_/datasets/scannet_seg-3d-20class.py --task lidar_seg --output-dir ${OUTPUT_DIR} --online
+python tools/misc/browse_dataset.py configs/_base_/datasets/scannet-seg.py --task lidar_seg --output-dir ${OUTPUT_DIR} --online
 ```

 ![](../../../resources/browse_dataset_seg.png)

--- a/docs/zh_cn/advanced_guides/datasets/scannet.md
+++ b/docs/zh_cn/advanced_guides/datasets/scannet.md
@@ -339,14 +339,14 @@ train_pipeline = [

 默认情况下，MMDet3D 的代码是在训练集上进行模型训练，然后在验证集上进行模型测试。

-如果你也想在在线基准上测试模型的性能（仅支持语义分割），请在测试命令中加上 `--format-only` 的标记，同时也要将 ScanNet 数据集[配置文件](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/_base_/datasets/scannet_seg-3d-20class.py#L126)中的 `ann_file=data_root + 'scannet_infos_val.pkl'` 改成 `ann_file=data_root + 'scannet_infos_test.pkl'`。
+如果你也想在在线基准上测试模型的性能（仅支持语义分割），请在测试命令中加上 `--format-only` 的标记，同时也要将 ScanNet 数据集[配置文件](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/_base_/datasets/scannet-seg.py#L126)中的 `ann_file=data_root + 'scannet_infos_val.pkl'` 改成 `ann_file=data_root + 'scannet_infos_test.pkl'`。

 请记得通过 `txt_prefix` 来指定想要保存测试结果的文件夹名称。

 以 PointNet++ (SSG) 在 ScanNet 上的测试为例，你可以运行以下命令来完成测试结果的保存：

 ```
-./tools/dist_test.sh configs/pointnet2/pointnet2_ssg_16x2_cosine_200e_scannet_seg-3d-20class.py \
+./tools/dist_test.sh configs/pointnet2/pointnet2_ssg_16x2_cosine_200e_scannet-seg.py \
    work_dirs/pointnet2_ssg/latest.pth --format-only \
    --eval-options txt_prefix=work_dirs/pointnet2_ssg/test_submission
 ```

--- a/docs/zh_cn/advanced_guides/supported_tasks/lidar_sem_seg3d.md
+++ b/docs/zh_cn/advanced_guides/supported_tasks/lidar_sem_seg3d.md
@@ -65,13 +65,13 @@ mmdetection3d
 此外，在训练完成后你也可以评估特定的模型权重文件。你可以简单地执行以下脚本：

 ```
-./tools/dist_test.sh configs/pointnet2/pointnet2_ssg_16x2_cosine_200e_scannet_seg-3d-20class.py work_dirs/pointnet2_ssg/latest.pth 8
+./tools/dist_test.sh configs/pointnet2/pointnet2_ssg_16x2_cosine_200e_scannet-seg.py work_dirs/pointnet2_ssg/latest.pth 8
 ```

 ## 测试与提交

 如果你只想在在线基准上进行推理或测试模型性能，你需要在配置文件中的 `test_evalutor` 字段增加 `submission_prefix`， 例如配置文件增加 `test_evaluator = dict(type='SegMetric',submission_prefix=work_dirs/pointnet2_ssg/test_submission`)。
-并将 ScanNet 数据集[配置文件](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/_base_/datasets/scannet_seg-3d-20class.py#L129)中的 `ann_file=scannet_infos_val.pkl` 变成 `ann_file=scannet_infos_test.pkl`。在生成结果后，你可以压缩文件夹并上传至 [ScanNet 评估服务器](http://kaldir.vc.in.tum.de/scannet_benchmark/semantic_label_3d)上。
+并将 ScanNet 数据集[配置文件](https://github.com/open-mmlab/mmdetection3d/blob/main/configs/_base_/datasets/scannet-seg.py#L129)中的 `ann_file=scannet_infos_val.pkl` 变成 `ann_file=scannet_infos_test.pkl`。在生成结果后，你可以压缩文件夹并上传至 [ScanNet 评估服务器](http://kaldir.vc.in.tum.de/scannet_benchmark/semantic_label_3d)上。

 ## 定性评估