Rascene：利用毫米波通信信号的高保真三维场景成像
Rascene: High-Fidelity 3D Scene Imaging with mmWave Communication Signals
用毫米波 OFDM 通信信号替代相机/激光雷达做 3D 重建，其多帧空间自适应融合与置信度加权前向投影可迁移到稀疏、多径歧义观测下的点云/深度融合与位姿无关的几何一致性聚合。
原文依据 Rascene performs multi-frame, spatially adaptive fusion with confidence-weighted forward projection
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论稳健的三维环境感知对于自动驾驶和机器人导航等应用至关重要。然而，相机和激光雷达等光学传感器在烟雾、雾霾和非理想光照等恶劣条件下往往失效。尽管专用雷达系统可以在这些环境中工作，但其对定制硬件和授权频谱的依赖限制了可扩展性与成本效益。本文提出 Rascene，一个利用无处不在的毫米波 OFDM 通信信号进行三维场景成像的通信感知一体化（ISAC）框架。为克服单帧无线电数据稀疏且存在多径歧义的问题，Rascene 采用置信度加权前向投影进行多帧、空间自适应融合，从而能够在任意位姿下恢复几何一致性。实验结果表明，本方法能够高精度地重建三维场景，为低成本、可扩展且稳健的三维感知提供了一条新路径。
英文原文摘要 1007 字符
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.