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}
}
| Method | Backbone | Lr schd | Mem (GB) | Inf time (fps) | AP@0.25 | AP@0.5 | Download |
|---|---|---|---|---|---|---|---|
| L6, O256 | PointNet++ | 3x | 6.7 | 66.32 (65.67*) | 47.82 (47.74*) | model | log | |
| L12, O256 | PointNet++ | 3x | 9.4 | 66.57 (66.22*) | 48.21 (48.95*) | model | log | |
| L12, O256 | PointNet++w2x | 3x | 13.3 | 68.20 (67.30*) | 51.02 (50.44*) | model | log | |
| L12, O512 | PointNet++w2x | 3x | 18.8 | 68.22 (68.20*) | 52.61 (51.31*) | model | log |
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.