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arXiv · 2610.37929

3D Occupancy-Language Fields from mmWave Radar

Haowen Lai·Zhiwei Zheng·Mingmin Zhao
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Abstract

Radar systems can recover scene geometry and recognize predefined categories, but these outputs do not provide a persistent spatial representation for open-ended language queries. Such queries require linking observations from different viewpoints to the same physical locations. We present TextRadar, a framework that constructs a scene-level 3D occupancy-language field from panoramic radar observations. The field combines occupancy with scale-dependent language features at surface locations, allowing the reconstructed scene to be queried with new descriptions. A single-frame radar distillation model predicts aligned ranges and language features from each radar observation. Using radar-estimated poses, we register these predictions to reconstruct the field from radar alone in a new scene. Experiments on approximately 20k synchronized observations across seven buildings demonstrate scene-level natural-language querying and prompt-free semantic visualization. Analysis of camera-to-radar concept transferability shows stronger agreement for structures, materials, and human poses than for colors and printed text.

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Haowen Lai, Zhiwei Zheng, Mingmin Zhao. 2026-10-27. 3D Occupancy-Language Fields from mmWave Radar. https://arxiv.org/abs/2610.37929

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