TeFF: Tracking-enhanced Forgetting-free Few-shot 3D LiDAR Semantic Segmentation
<|reference_start|>TeFF: Tracking-enhanced Forgetting-free Few-shot 3D LiDAR Semantic Segmentation: In autonomous driving, 3D LiDAR plays a crucial role in understanding the vehicle's surroundings. However, the newly emerged, unannotated objects presents few-shot learning problem for semantic segmentation. This paper addresses the limitations of current few-shot semantic segmentation by exploiting the temporal continuity of LiDAR data. Employing a tracking model to generate pseudo-ground-truths from a sequence of LiDAR frames, our method significantly augments the dataset, enhancing the model's ability to learn on novel classes. However, this approach introduces a data imbalance biased to novel data that presents a new challenge of catastrophic forgetting. To mitigate this, we incorporate LoRA, a technique that reduces the number of trainable parameters, thereby preserving the model's performance on base classes while improving its adaptability to novel classes. This work represents a significant step forward in few-shot 3D LiDAR semantic segmentation for autonomous driving. Our code is available at https://github.com/junbao-zhou/Track-no-forgetting.<|reference_end|>
@article{zhou2024teff:,
    title={TeFF: Tracking-enhanced Forgetting-free Few-shot 3D LiDAR Semantic
  Segmentation},
    author={Junbao Zhou, Jilin Mei, Pengze Wu, Liang Chen, Fangzhou Zhao, Xijun
  Zhao, Yu Hu},
    journal={arXiv preprint arXiv:2408.15657},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.15657},
    primaryClass={cs.CV cs.RO}
}