Smi-Supervised Domain Adaptation Using Target-Oriented Domain Augmentation for 3D Object Detection

IEEE Transaction on Inteeligent Vehicles

Yecheol Kim^1*   Junho Lee^1*   Changsoo Park²    Hyung won Kim²   Inho Lim²   Christopher Chang²   Jun Won Choi³

¹ Hanyang University ² Kakao Mobility Corp ³ Seoul National Univeristy

We prospose novel two-stage SSDA framework for 3D object detection TODA. TODA achieves SOTA on Waymo to nuScenes domain adaptation benchmarks, attains performances on par with the Oracle performance utilizing merely 5% of labeled data in the target domain.

Main Results

Waymo to nuScenes

We utilizes 100% of Waymo annotations along with partial nuScenes annotations. For nuScenes, we uniformly downsample the training samples to 0.1%, 1%, 5%, and 10% (resulting in 28, 282, 1,407, 2,813, and frames respectively), while the remaining samples are left unlabeled.

Methods	0.1%	0.5%	1%	5%	10%
Labeled Target	fail	36.0 / 37.7	37.2 / 38.1	61.0 / 53.2	65.6 / 58.2
SSDA3D	62.0 / 57.4	70.3 / 65.1	73.4 / 67.1	76.2 / 68.8	78.8 / 70.9
Ours	69.7 / 63.6	73.7 / 67.3	75.6 / 68.5	79.0 / 71.1	78.8 / 70.9
Oracle	78.4 / 69.9	78.4 / 69.9	78.4 / 69.9	78.4 / 69.9	78.4 / 69.9

Citation

If you find this work or code useful, please cite

@article{kim2024semi,
  title={Semi-Supervised Domain Adaptation Using Target-Oriented Domain Augmentation for 3D Object Detection},
  author={Kim, Yecheol and Lee, Junho and Park, Changsoo and won Kim, Hyoung and Lim, Inho and Chang, Christopher and Choi, Jun Won},
  journal={IEEE Transactions on Intelligent Vehicles},
  year={2024},
  publisher={IEEE}
}

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