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

arXiv 2604.02603 ↗First posted 3 Apr 2026cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 29 of its 50 references that are in the map. First posted on arXiv 3 Apr 2026.

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29 of 50 references.

References · 29

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17 more
  1. 2024MegaSaM: Accurate, Fast, and Robust Structure and Motion from Casual Dynamic Videoscited by 228
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  12. 2022Sensing as a Service in 6G Perceptive Networks: A Unified Framework for ISAC Resource Allocationcited by 7
  13. 2025Towards Foundational Models for Single-Chip Radarcited by 6
  14. 2022Full-Duplex Communication for ISAC: Joint Beamforming and Power Optimizationcited by 5
  15. 2022Toward Integrated Sensing and Communications in IEEE 802.11bf Wi-Fi Networkscited by 5
  16. 2024Fundamental Trade-Offs in Monostatic ISAC: A Holistic Investigation Towards 6Gcited by 2
  17. 2026Spectrum Shortage for Radio Sensing? Leveraging Ambient 5G Signals for Human Activity Detectioncited by 1

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