Add support for VoxelNeXt (open-mmlab#1309)

* VoxelNeXt
xq198109 · Apr 3, 2023 · 83954d0 · 83954d0
1 parent 31f6758
commit 83954d0
Show file tree

Hide file tree

Showing 33 changed files with 136,204 additions and 19 deletions.
diff --git a/README.md b/README.md
@@ -22,6 +22,8 @@ It is also the official code release of [`[PointRCNN]`](https://arxiv.org/abs/18
 
 
 ## Changelog
+[2023-04-02] Added support for [`VoxelNeXt`](https://github.com/dvlab-research/VoxelNeXt) on Nuscenes, Waymo, and Argoverse2 datasets. It is a fully sparse 3D object detection network, which is a clean sparse CNNs network and predicts 3D objects directly upon voxels.
+
 [2022-09-02] **NEW:** Update `OpenPCDet` to v0.6.0:
 * Official code release of [MPPNet](https://arxiv.org/abs/2205.05979) for temporal 3D object detection, which supports long-term multi-frame 3D object detection and ranks 1st place on [3D detection learderboard](https://waymo.com/open/challenges/2020/3d-detection) of Waymo Open Dataset on Sept. 2th, 2022. For validation dataset, MPPNet achieves 74.96%, 75.06% and 74.52% for vehicle, pedestrian and cyclist classes in terms of mAPH@Level_2. (see the [guideline](docs/guidelines_of_approaches/mppnet.md) on how to train/test with MPPNet).
 * Support multi-frame training/testing on Waymo Open Dataset (see the [change log](docs/changelog.md) for more details on how to process data).
@@ -172,14 +174,14 @@ By default, all models are trained with **a single frame** of **20% data (~32k f
 | [PV-RCNN++](tools/cfgs/waymo_models/pv_rcnn_plusplus.yaml) | 77.82/77.32|	69.07/68.62|	77.99/71.36|	69.92/63.74|	71.80/70.71|	69.31/68.26|
 | [PV-RCNN++ (ResNet)](tools/cfgs/waymo_models/pv_rcnn_plusplus_resnet.yaml) |77.61/77.14|	69.18/68.75|	79.42/73.31|	70.88/65.21|	72.50/71.39|	69.84/68.77|
 
-
 Here we also provide the performance of several models trained on the full training set (refer to the paper of [PV-RCNN++](https://arxiv.org/abs/2102.00463)):
 
 |    Performance@(train with 100\% Data)            | Vec_L1 | Vec_L2 | Ped_L1 | Ped_L2 | Cyc_L1 | Cyc_L2 |  
 |---------------------------------------------|----------:|:-------:|:-------:|:-------:|:-------:|:-------:|
 | [SECOND](tools/cfgs/waymo_models/second.yaml) | 72.27/71.69 | 63.85/63.33 | 68.70/58.18 | 60.72/51.31 | 60.62/59.28 | 58.34/57.05 | 
 | [CenterPoint-Pillar](tools/cfgs/waymo_models/centerpoint_pillar_1x.yaml)| 73.37/72.86 | 65.09/64.62 | 75.35/65.11 | 67.61/58.25 | 67.76/66.22 | 65.25/63.77 | 
 | [Part-A2-Anchor](tools/cfgs/waymo_models/PartA2.yaml) | 77.05/76.51 | 68.47/67.97 | 75.24/66.87 | 66.18/58.62 | 68.60/67.36 | 66.13/64.93 |
+| [VoxelNeXt-2D](tools/cfgs/waymo_models/voxelnext2d_ioubranch.yaml) | 77.94/77.47	|69.68/69.25	|80.24/73.47	|72.23/65.88	|73.33/72.20	|70.66/69.56 | 
 | [PV-RCNN (CenterHead)](tools/cfgs/waymo_models/pv_rcnn_with_centerhead_rpn.yaml) | 78.00/77.50 | 69.43/68.98 | 79.21/73.03 | 70.42/64.72 | 71.46/70.27 | 68.95/67.79 |
 | [PV-RCNN++](tools/cfgs/waymo_models/pv_rcnn_plusplus.yaml) | 79.10/78.63 | 70.34/69.91 | 80.62/74.62 | 71.86/66.30 | 73.49/72.38 | 70.70/69.62 |
 | [PV-RCNN++ (ResNet)](tools/cfgs/waymo_models/pv_rcnn_plusplus_resnet.yaml) | 79.25/78.78 | 70.61/70.18 | 81.83/76.28 | 73.17/68.00 | 73.72/72.66 | 71.21/70.19 |
@@ -198,13 +200,14 @@ but you could easily achieve similar performance by training with the default co
 ### NuScenes 3D Object Detection Baselines
 All models are trained with 8 GTX 1080Ti GPUs and are available for download.
 
-|                                             | mATE | mASE | mAOE | mAVE | mAAE | mAP | NDS | download | 
-|---------------------------------------------|----------:|:-------:|:-------:|:-------:|:---------:|:-------:|:-------:|:---------:|
-| [PointPillar-MultiHead](tools/cfgs/nuscenes_models/cbgs_pp_multihead.yaml) | 33.87	| 26.00 | 32.07	| 28.74 | 20.15 | 44.63 | 58.23	 | [model-23M](https://drive.google.com/file/d/1p-501mTWsq0G9RzroTWSXreIMyTUUpBM/view?usp=sharing) | 
-| [SECOND-MultiHead (CBGS)](tools/cfgs/nuscenes_models/cbgs_second_multihead.yaml) | 31.15 |	25.51 |	26.64 | 26.26 | 20.46 | 50.59 | 62.29 | [model-35M](https://drive.google.com/file/d/1bNzcOnE3u9iooBFMk2xK7HqhdeQ_nwTq/view?usp=sharing) |
-| [CenterPoint-PointPillar](tools/cfgs/nuscenes_models/cbgs_dyn_pp_centerpoint.yaml) | 31.13 |	26.04 |	42.92 | 23.90 | 19.14 | 50.03 | 60.70 | [model-23M](https://drive.google.com/file/d/1UvGm6mROMyJzeSRu7OD1leU_YWoAZG7v/view?usp=sharing) |
-| [CenterPoint (voxel_size=0.1)](tools/cfgs/nuscenes_models/cbgs_voxel01_res3d_centerpoint.yaml) | 30.11 |	25.55 |	38.28 | 21.94 | 18.87 | 56.03 | 64.54 | [model-34M](https://drive.google.com/file/d/1Cz-J1c3dw7JAWc25KRG1XQj8yCaOlexQ/view?usp=sharing) |
-| [CenterPoint (voxel_size=0.075)](tools/cfgs/nuscenes_models/cbgs_voxel0075_res3d_centerpoint.yaml) | 28.80 |	25.43 |	37.27 | 21.55 | 18.24 | 59.22 | 66.48 | [model-34M](https://drive.google.com/file/d/1XOHAWm1MPkCKr1gqmc3TWi5AYZgPsgxU/view?usp=sharing) |
+|                                                                                                    |   mATE |  mASE  |  mAOE  | mAVE  | mAAE  |  mAP  |  NDS   |                                              download                                              | 
+|----------------------------------------------------------------------------------------------------|-------:|:------:|:------:|:-----:|:-----:|:-----:|:------:|:--------------------------------------------------------------------------------------------------:|
+| [PointPillar-MultiHead](tools/cfgs/nuscenes_models/cbgs_pp_multihead.yaml)                         | 33.87	 | 26.00  | 32.07	 | 28.74 | 20.15 | 44.63 | 58.23	 |  [model-23M](https://drive.google.com/file/d/1p-501mTWsq0G9RzroTWSXreIMyTUUpBM/view?usp=sharing)   | 
+| [SECOND-MultiHead (CBGS)](tools/cfgs/nuscenes_models/cbgs_second_multihead.yaml)                   |  31.15 | 	25.51 | 	26.64 | 26.26 | 20.46 | 50.59 | 62.29  |  [model-35M](https://drive.google.com/file/d/1bNzcOnE3u9iooBFMk2xK7HqhdeQ_nwTq/view?usp=sharing)   |
+| [CenterPoint-PointPillar](tools/cfgs/nuscenes_models/cbgs_dyn_pp_centerpoint.yaml)                 |  31.13 | 	26.04 | 	42.92 | 23.90 | 19.14 | 50.03 | 60.70  |  [model-23M](https://drive.google.com/file/d/1UvGm6mROMyJzeSRu7OD1leU_YWoAZG7v/view?usp=sharing)   |
+| [CenterPoint (voxel_size=0.1)](tools/cfgs/nuscenes_models/cbgs_voxel01_res3d_centerpoint.yaml)     |  30.11 | 	25.55 | 	38.28 | 21.94 | 18.87 | 56.03 | 64.54  |  [model-34M](https://drive.google.com/file/d/1Cz-J1c3dw7JAWc25KRG1XQj8yCaOlexQ/view?usp=sharing)   |
+| [CenterPoint (voxel_size=0.075)](tools/cfgs/nuscenes_models/cbgs_voxel0075_res3d_centerpoint.yaml) |  28.80 | 	25.43 | 	37.27 | 21.55 | 18.24 | 59.22 | 66.48  |  [model-34M](https://drive.google.com/file/d/1XOHAWm1MPkCKr1gqmc3TWi5AYZgPsgxU/view?usp=sharing)   |
+| [VoxelNeXt (voxel_size=0.075)](tools/cfgs/nuscenes_models/cbgs_voxel0075_voxelnext.yaml)   |  30.11 | 	25.23 | 	40.57 | 21.69 | 18.56 | 60.53 | 66.65  | [model-31M](https://drive.google.com/file/d/1IV7e7G9X-61KXSjMGtQo579pzDNbhwvf/view?usp=share_link) |
 
 
 ### ONCE 3D Object Detection Baselines
@@ -218,6 +221,14 @@ All models are trained with 8 GPUs.
 | [PV-RCNN](tools/cfgs/once_models/pv_rcnn.yaml)         | 77.77   | 23.50      | 59.37   | 53.55  |
 | [CenterPoint](tools/cfgs/once_models/centerpoint.yaml) | 78.02   | 49.74      | 67.22   | 64.99  |
 
+### Argoverse2 3D Object Detection Baselines
+All models are trained with 4 GPUs.
+
+|                                                         | mAP  |                                              download                                              | 
+|---------------------------------------------------------|:----:|:--------------------------------------------------------------------------------------------------:|
+| [VoxelNeXt](tools/cfgs/argo2_models/cbgs_voxel01_voxelnext.yaml)        | 30.0 | [model-30M](https://drive.google.com/file/d/1zr-it1ERJzLQ3a3hP060z_EQqS_RkNaC/view?usp=share_link) | 
+| [VoxelNeXt-K3](tools/cfgs/argo2_models/cbgs_voxel01_voxelnext_headkernel3.yaml) | 30.7 | [model-45M](https://drive.google.com/file/d/1NrYRsiKbuWyL8jE4SY27IHpFMY9K0o__/view?usp=share_link) | 
+
 ### Other datasets
 Welcome to support other datasets by submitting pull request.