Auto-Vocabulary Segmentation for LiDAR Points
<|reference_start|>Auto-Vocabulary Segmentation for LiDAR Points: Existing perception methods for autonomous driving fall short of recognizing unknown entities not covered in the training data. Open-vocabulary methods offer promising capabilities in detecting any object but are limited by user-specified queries representing target classes. We propose AutoVoc3D, a framework for automatic object class recognition and open-ended segmentation. Evaluation on nuScenes showcases AutoVoc3D's ability to generate precise semantic classes and accurate point-wise segmentation. Moreover, we introduce Text-Point Semantic Similarity, a new metric to assess the semantic similarity between text and point cloud without eliminating novel classes.<|reference_end|>
@article{wei2024auto-vocabulary,
    title={Auto-Vocabulary Segmentation for LiDAR Points},
    author={Weijie Wei, Osman "Ulger, Fatemeh Karimi Nejadasl, Theo Gevers,
  Martin R. Oswald},
    journal={arXiv preprint arXiv:2406.09126},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.09126},
    primaryClass={cs.CV}
}