用毫米波通信信号实现高精度3D场景成像，无需专用硬件。
Rascene: High-Fidelity 3D Scene Imaging with mmWave Communication Signals
- 利用现有毫米波通信信号，融合多帧数据重建三维场景。
- 在复杂环境下实现厘米级精度的3D重建，优于传统雷达。
- 适合自动驾驶、机器人导航等需要低成本鲁棒感知的场景。
稳健的3D环境感知对自动驾驶和机器人导航至关重要。然而，摄像头和激光雷达等光学传感器在烟雾、雾气及非理想光照条件下常失效。尽管专用雷达系统可在这些环境中运行，但其依赖定制硬件和授权频谱，限制了可扩展性和成本效益。本文提出Rascene，一种集成感知与通信（ISAC）框架，利用普遍存在的毫米波OFDM通信信号实现3D场景成像。为克服单帧信号稀疏且存在多径模糊的问题，Rascene采用多帧、空间自适应融合与置信加权前向投影，实现任意姿态下的几何一致性恢复。实验结果表明，该方法可实现高精度3D场景重建，为低成本、可扩展、鲁棒的3D感知提供了新路径。
原文摘要 · Abstract (English)
Robust 3D environmental perception is critical for applications such as autonomous driving and robot navigation. However, optical sensors such as cameras and LiDAR often fail under adverse conditions, including smoke, fog, and non-ideal lighting. Although specialized radar systems can operate in these environments, their reliance on bespoke hardware and licensed spectrum limits scalability and cost-effectiveness. This paper introduces Rascene, an integrated sensing and communication (ISAC) framework that leverages ubiquitous mmWave OFDM communication signals for 3D scene imaging. To overcome the sparse and multipath-ambiguous nature of individual radio frames, Rascene performs multi-frame, spatially adaptive fusion with confidence-weighted forward projection, enabling the recovery of geometric consensus across arbitrary poses. Experimental results demonstrate that our method reconstructs 3D scenes with high precision, offering a new pathway toward low-cost, scalable, and robust 3D perception.
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