Talk2Radar: Bridging Natural Language with 4D mmWave Radar for 3D Referring Expression Comprehension
<|reference_start|>Talk2Radar: Bridging Natural Language with 4D mmWave Radar for 3D Referring Expression Comprehension: Embodied perception is essential for intelligent vehicles and robots in interactive environmental understanding. However, these advancements primarily focus on vision, with limited attention given to using 3D modeling sensors, restricting a comprehensive understanding of objects in response to prompts containing qualitative and quantitative queries. Recently, as a promising automotive sensor with affordable cost, 4D millimeter-wave radars provide denser point clouds than conventional radars and perceive both semantic and physical characteristics of objects, thereby enhancing the reliability of perception systems. To foster the development of natural language-driven context understanding in radar scenes for 3D visual grounding, we construct the first dataset, Talk2Radar, which bridges these two modalities for 3D Referring Expression Comprehension (REC). Talk2Radar contains 8,682 referring prompt samples with 20,558 referred objects. Moreover, we propose a novel model, T-RadarNet, for 3D REC on point clouds, achieving State-Of-The-Art (SOTA) performance on the Talk2Radar dataset compared to counterparts. Deformable-FPN and Gated Graph Fusion are meticulously designed for efficient point cloud feature modeling and cross-modal fusion between radar and text features, respectively. Comprehensive experiments provide deep insights into radar-based 3D REC. We release our project at https://github.com/GuanRunwei/Talk2Radar.<|reference_end|>
@article{guan2024talk2radar:,
    title={Talk2Radar: Bridging Natural Language with 4D mmWave Radar for 3D
  Referring Expression Comprehension},
    author={Runwei Guan, Ruixiao Zhang, Ningwei Ouyang, Jianan Liu, Ka Lok Man,
  Xiaohao Cai, Ming Xu, Jeremy Smith, Eng Gee Lim, Yutao Yue, Hui Xiong},
    journal={arXiv preprint arXiv:2405.12821},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.12821},
    primaryClass={cs.RO cs.CV}
}