Rascene: High-Fidelity 3D Scene Imaging with mmWave Communication Signals
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Acceso en línea: | |
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| _version_ | 1866908934547177472 |
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| author | Song, Kunzhe Zhou, Geo Jie Liu, Xiaoming Zeng, Huacheng |
| author_facet | Song, Kunzhe Zhou, Geo Jie Liu, Xiaoming Zeng, Huacheng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_02603 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Rascene: High-Fidelity 3D Scene Imaging with mmWave Communication Signals Song, Kunzhe Zhou, Geo Jie Liu, Xiaoming Zeng, Huacheng Computer Vision and Pattern Recognition 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. |
| title | Rascene: High-Fidelity 3D Scene Imaging with mmWave Communication Signals |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.02603 |