# Group-Free 3D Object Detection via Transformers

## Introduction

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We implement Group-Free-3D and provide the result and checkpoints on ScanNet datasets.

```
@article{liu2021,
  title={Group-Free 3D Object Detection via Transformers},
  author={Liu, Ze and Zhang, Zheng and Cao, Yue and Hu, Han and Tong, Xin},
  journal={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2021}
}
```

## Results

### ScanNet

|  Method  |  Backbone   | Lr schd | Mem (GB) | Inf time (fps) | AP@0.25 |AP@0.5| Download |
| :------: | :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: |
| [L6, O256](./groupfree3d_8x4_scannet-3d-18class-L6-O256.py ) |    PointNet++     |  3x    |6.7||66.32 (65.67*)|47.82 (47.74*)|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/groupfree3d/groupfree3d_8x4_scannet-3d-18class-L6-O256/groupfree3d_8x4_scannet-3d-18class-L6-O256_20210702_145347-3499eb55.pth) &#124; [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/groupfree3d/groupfree3d_8x4_scannet-3d-18class-L6-O256/groupfree3d_8x4_scannet-3d-18class-L6-O256_20210702_145347.log.json)|
| [L12, O256](./groupfree3d_8x4_scannet-3d-18class-L12-O256.py ) |    PointNet++     |  3x    |9.4||66.57 (66.22*)|48.21 (48.95*)|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/groupfree3d/groupfree3d_8x4_scannet-3d-18class-L12-O256/groupfree3d_8x4_scannet-3d-18class-L12-O256_20210702_150907-1c5551ad.pth) &#124; [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/groupfree3d/groupfree3d_8x4_scannet-3d-18class-L12-O256/groupfree3d_8x4_scannet-3d-18class-L12-O256_20210702_150907.log.json)|
| [L12, O256](./groupfree3d_8x4_scannet-3d-18class-w2x-L12-O256.py ) |    PointNet++w2x     |  3x    |13.3||68.20 (67.30*)|51.02 (50.44*)|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/groupfree3d/groupfree3d_8x4_scannet-3d-18class-w2x-L12-O256/groupfree3d_8x4_scannet-3d-18class-w2x-L12-O256_20210702_200301-944f0ac0.pth) &#124; [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/groupfree3d/groupfree3d_8x4_scannet-3d-18class-w2x-L12-O256/groupfree3d_8x4_scannet-3d-18class-w2x-L12-O256_20210702_200301.log.json)|
| [L12, O512](./groupfree3d_8x4_scannet-3d-18class-w2x-L12-O512.py ) |    PointNet++w2x     |  3x    |18.8||68.22 (68.20*)|52.61 (51.31*)|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/groupfree3d/groupfree3d_8x4_scannet-3d-18class-w2x-L12-O512/groupfree3d_8x4_scannet-3d-18class-w2x-L12-O512_20210702_220204-187b71c7.pth) &#124; [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/groupfree3d/groupfree3d_8x4_scannet-3d-18class-w2x-L12-O512/groupfree3d_8x4_scannet-3d-18class-w2x-L12-O512_20210702_220204.log.json)|

**Notes:**

- We report the best results (AP@0.50) on validation set during each training. * means the evaluation method in the paper: we train each setting 5 times and test each training trial 5 times, then the average performance of these 25 trials is reported to account for algorithm randomness.
- We use 4 GPUs for training by default as the original code.
