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Group-Free 3D Object Detection via Transformers

Introduction

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={arXiv preprint arXiv:2104.00678},
  year={2021}
}

Results

ScanNet

Method Backbone Lr schd Mem (GB) Inf time (fps) [email protected] [email protected] 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 ([email protected]) 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.