Talk2Radar: Bridging Natural Language with 4D mmWave Radar for 3D Referring Expression Comprehension

Runwei Guan, Ruixiao Zhang, Ningwei Ouyang, Jianan Liu, Ka Lok Man, Xiaohao Cai, Ming Xu, Jeremy S. Smith, Eng Gee Lim, Yutao Yue, Hui Xiong

Research output: Chapter in Book or Report/Conference proceedingConference Proceeding

Abstract

Embodied perception is essential for intelligent vehicles and robots, enabling more natural interaction and task execution. However, these advancements currently embrace vision level, rarely focusing on using 3D modeling sensors, which limits the full understanding of surrounding objects with multi-granular characteristics. Recently, as a promising automotive sensor with affordable cost, 4D Millimeter-Wave radar provides denser point clouds than conventional radar and perceives both semantic and physical characteristics of objects, thus enhancing the reliability of perception system. To foster the development of natural language-driven context understanding in radar scenes for 3D grounding, we construct the first dataset, Talk2Radar, which bridges these two modalities for 3D Referring Expression Comprehension. Talk2Radar contains 8,682 referring prompt samples with 20,558 referred objects. Moreover, we propose a novel model, T-RadarNet for 3D REC upon point clouds, achieving state-of-the-art performances on Talk2Radar dataset compared with counterparts, where 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. Further, comprehensive experiments are conducted to give a deep insight into radar-based 3D REC. We release our project at https://github.com/GuanRunwei/Talk2Radar.
Original languageEnglish
Title of host publication42th IEEE International Conference on Robotics and Automation (ICRA 2025)
DOIs
Publication statusSubmitted - 2024

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