An official implementation of AAAI2023 paper "LWSIS: LiDAR-guided Weakly Supervised Instance Segmentation for Autonomous Driving"
| Model | Backbone | Annotations | Lr_schedule | Mask_AP | Download |
|---|---|---|---|---|---|
| BoxInst | R-50 | box | 1x | 33.65 | link(访问码：pmw0) |
| BoxInst | R-101 | box | 1x | 34.39 | link |
| PointSup | R-50 | box+point | 1x | 43.80 | link |
| PointSup | R-101 | box+point | 1x | 44.72 | link |
| LWSIS+BoxInst | R-50 | 3dbox+pc | 1x | 35.65 | link(访问码：hy6a) |
| LWSIS+BoxInst | R-101 | 3dbox+pc | 1x | 36,22 | link |
| LWSIS+PointSup | R-50 | 3dbox+pc | 1x | 45.46 | link |
| LWSIS+PointSup | R-101 | 3dbox+pc | 1x | 46.17 | link |
Here we explain different annotations used in the exp. 'box' means only using the 2D bounding box annotation for each instance, 'point' means using a specific number of points with human annotation indicating the background/foreground, '3dbox' means using the 3D bounding box annotations for each instance and 'pc' means the original point cloud.
First install Detectron2 following the official guide: INSTALL.md.
Please use Detectron2 with commit id 9eb4831 if you have any issues related to Detectron2.
Then build LWSIS with:
git clone [email protected]:Serenos/LWSIS.git
cd LWSIS
python setup.py build develop
-
Download the nuscenes origin datasets to ${HOME}/datasets/. The folder structure shall be like this:
- nuscenes
- annotations
- lidarseg
- maps
- samples
- sweeps
- v1.0-trainval
- v1.0-mini
- nuscenes
-
Download nuInsSeg3d_train(访问码：4aml), nuInsSeg3d_val(访问码：luw8) and put it into the nuscenes/annotations folder.
-
Training
bash tools/train.sh configs/BoxInst/MS_R_50_1x_nuscenes.yaml Boxinst_LWSIS 000 -
Evaluation
bash tools/test.sh configs/BoxInst/MS_R_50_1x_nuscenes.yaml output/Boxinst_LWSIS/000/model_final.pth
We supplement instance mask annotation for nuScenes dataset. For more detail, please follow the nuinsseg-devkit.
The authors are grateful to School of Computer Science, Beijing Institute of Technology, Shanghai AI Laboratory, Inceptio, 4SKL-IOTSC, CIS, University of Macau.
The code is based on Adlaidet.
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