  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.
