Leveraging LLMs for Enhanced Open-Vocabulary 3D Scene Understanding in Autonomous Driving
<|reference_start|>Leveraging LLMs for Enhanced Open-Vocabulary 3D Scene Understanding in Autonomous Driving: This paper introduces a novel method for open-vocabulary 3D scene understanding in autonomous driving by combining Language Embedded 3D Gaussians with Large Language Models (LLMs) for enhanced inference. We propose utilizing LLMs to generate contextually relevant canonical phrases for segmentation and scene interpretation. Our method leverages the contextual and semantic capabilities of LLMs to produce a set of canonical phrases, which are then compared with the language features embedded in the 3D Gaussians. This LLM-guided approach significantly improves zero-shot scene understanding and detection of objects of interest, even in the most challenging or unfamiliar environments. Experimental results on the WayveScenes101 dataset demonstrate that our approach surpasses state-of-the-art methods in terms of accuracy and flexibility for open-vocabulary object detection and segmentation. This work represents a significant advancement towards more intelligent, context-aware autonomous driving systems, effectively bridging 3D scene representation with high-level semantic understanding.<|reference_end|>
@article{chahe2024leveraging,
    title={Leveraging LLMs for Enhanced Open-Vocabulary 3D Scene Understanding in
  Autonomous Driving},
    author={Amirhosein Chahe, Lifeng Zhou},
    journal={arXiv preprint arXiv:2408.03516},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.03516},
    primaryClass={cs.CV cs.LG cs.RO}
}