This is the official implementation of the CVPR 2023 paper - GeoMAE: Masked Geometric Target Prediction for Self-supervised Point Cloud Pre-Training [https://arxiv.org/abs/2305.08808]
CUDA=11.3
python=3.8
pytorch=1.10.1
mmcv=1.4.8
mmdetection=2.20.0
mmdetection3d=0.15.0
spconv-cu113=2.1.21
ATTENTION: It is highly recommended to use the same version of these packages to avoid code mismatch.
For mmcv, you can follow the official installation.md to install the expected version.
For mmdetection and mmdetection3d, you can follow the official installation.md.
Finally, run
python setup.py develop
-
Prepare nuscenes or waymo data. We recommend you follow the MMdetection3D's instructions
-
Prepare nuscenes ssl data by running:
python tools/create_data.py nuscenes_ssl --root-path ./data/nuscenes --out-dir ./data/nuscenes --extra-tag nuscenes_ssl
- Use GeoMAE to pretrain the SST backbone:
./tools/dist_train.sh configs/mae_sst/m_sst_nus_singlestage_curv_07_ssl_dataset_wo_dbsampler_6x_1e-5.py 8
- Use the pretrained SST to train the PointPillar:
./tools/dist_train.sh configs/pre_sst/m_sst_nus_second_pointpillar_fpn355_222_curv_07_ssl_data_wo_dbsampler_6x_1e-5.py 8
You can load the pretrained GeoMAE to train the PointPillar.
| model name | weight | mAP | NDS |
|---|---|---|---|
| GeoMAE | Google Drive | - | - |
| GeoMAE-PP | Google Drive | 53.77 | 57.23 |