IEEE Transaction on Inteeligent Vehicles
Yecheol Kim1* Junho Lee1* Changsoo Park2 Hyung won Kim2 Inho Lim2 Christopher Chang2 Jun Won Choi3
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.
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 |
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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