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GeoMAE

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This is the official implementation of the paper - GeoMAE: Masked Geometric Target Prediction for Self-supervised Point Cloud Pre-Training

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# GeoMAE
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]

## Installation

### Requirement
```
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](https://github.com/open-mmlab/mmcv/blob/main/docs/en/get_started/installation.md) to install the expected version.

For mmdetection and mmdetection3d, you can follow the official [installation.md](https://mmdetection3d.readthedocs.io/en/latest/get_started.html).

Finally, run
```
python setup.py develop
```

## Dataset preparation

1. Prepare nuscenes or waymo data. We recommend you follow the MMdetection3D's [instructions](https://mmdetection3d.readthedocs.io/en/latest/user_guides/dataset_prepare.html)

2. 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
```

## Training

### nuScenes
1. 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
```

2. 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
```

## CheckPoint
You can load the pretrained GeoMAE to train the PointPillar.

model name | weight | mAP | NDS | 
------|:--:| :--: | :--:|
GeoMAE | [Google Drive](https://drive.google.com/file/d/1qgCHf5C7ilVm3IJje_hja9m3oo-_uBkd/view?usp=drive_link) | - | - | 
GeoMAE-PP | [Google Drive](https://drive.google.com/file/d/1MYIj0sepo9kaqLzPdV8KJ0zYNmFwEW9d/view?usp=drive_link) | 53.77 | 57.23 |

GeoMAE

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