Rascene: High-Fidelity 3D Scene Imaging with mmWave Communication Signals

TL;DR

Rascene utilizes mmWave communication signals to achieve high-fidelity 3D scene imaging, providing a robust alternative to optical sensors in adverse conditions.

Contribution

It introduces a novel ISAC framework that fuses multi-frame mmWave signals for accurate 3D perception, overcoming multipath ambiguity and hardware limitations.

Findings

Reconstructs 3D scenes with high precision in challenging environments.

Enables low-cost, scalable 3D perception using existing communication signals.

Demonstrates robustness where optical sensors fail.

Abstract

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.