TeFF: Tracking-enhanced Forgetting-free Few-shot 3D LiDAR Semantic Segmentation

• URL: http://arxiv.org/abs/2408.15657v1
• Date: Wed, 28 Aug 2024 09:18:36 GMT
• Title: TeFF: Tracking-enhanced Forgetting-free Few-shot 3D LiDAR Semantic Segmentation
• Authors: Junbao Zhou, Jilin Mei, Pengze Wu, Liang Chen, Fangzhou Zhao, Xijun Zhao, Yu Hu,
• Abstract summary: This paper addresses the limitations of current few-shot semantic segmentation by exploiting the temporal continuity of LiDAR data. We employ a tracking model to generate pseudo-ground-truths from a sequence of LiDAR frames, enhancing the dataset's ability to learn on novel classes. 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.
• Score: 10.628870775939161
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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