RFDT-Channel: RGB-LiDAR-Based RF Digital Twin Scene Construction for 28 GHz Indoor Ray-Tracing Channel Simulation
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910278563659776 |
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| author | Yao, Chengyang Pan, Cunhua Zeng, Jiaming Sun, Yuquan Weng, Haoyang Wang, Haojian Ren, Hong Wang, Jiangzhou |
| author_facet | Yao, Chengyang Pan, Cunhua Zeng, Jiaming Sun, Yuquan Weng, Haoyang Wang, Haojian Ren, Hong Wang, Jiangzhou |
| contents | Real-scene indoor millimeter-wave simulation requires efficient modeling of radio frequency (RF)-computable geometry and electromagnetic material properties. To address the low efficiency of manual scene modeling, the limited RF adaptability of visually reconstructed meshes, and the lack of material binding in 28 GHz ray-tracing simulation, RFDT-Channel is developed as an RF digital twin scene construction workflow based on red-green-blue (RGB) images and light detection and ranging (LiDAR) point clouds. Indoor videos and point clouds are collected by a Jetson Orin platform with LiDAR and GMSL cameras. An initial triangular mesh is generated through COLMAP, 3D Gaussian Splatting, and SuGaR. The LiDAR point cloud then provides geometric and scale references for RF-oriented regularization in Blender, including alignment, wall solidification, door/window opening construction, and topology repair. OpenScene semantic segmentation maps major indoor structures to concrete, glass, wood, and metal materials, and Sionna RT performs 28 GHz ray tracing. Under a fixed transmitter-receiver deployment, the generated channel impulse response (CIR), channel frequency response (CFR), and Radio Map results show that material binding mainly changes weak reflection, transmission, and scattering paths, reducing the number of effective paths from about 742 to about 52 while keeping the dominant path amplitude nearly unchanged. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_01261 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | RFDT-Channel: RGB-LiDAR-Based RF Digital Twin Scene Construction for 28 GHz Indoor Ray-Tracing Channel Simulation Yao, Chengyang Pan, Cunhua Zeng, Jiaming Sun, Yuquan Weng, Haoyang Wang, Haojian Ren, Hong Wang, Jiangzhou Image and Video Processing Real-scene indoor millimeter-wave simulation requires efficient modeling of radio frequency (RF)-computable geometry and electromagnetic material properties. To address the low efficiency of manual scene modeling, the limited RF adaptability of visually reconstructed meshes, and the lack of material binding in 28 GHz ray-tracing simulation, RFDT-Channel is developed as an RF digital twin scene construction workflow based on red-green-blue (RGB) images and light detection and ranging (LiDAR) point clouds. Indoor videos and point clouds are collected by a Jetson Orin platform with LiDAR and GMSL cameras. An initial triangular mesh is generated through COLMAP, 3D Gaussian Splatting, and SuGaR. The LiDAR point cloud then provides geometric and scale references for RF-oriented regularization in Blender, including alignment, wall solidification, door/window opening construction, and topology repair. OpenScene semantic segmentation maps major indoor structures to concrete, glass, wood, and metal materials, and Sionna RT performs 28 GHz ray tracing. Under a fixed transmitter-receiver deployment, the generated channel impulse response (CIR), channel frequency response (CFR), and Radio Map results show that material binding mainly changes weak reflection, transmission, and scattering paths, reducing the number of effective paths from about 742 to about 52 while keeping the dominant path amplitude nearly unchanged. |
| title | RFDT-Channel: RGB-LiDAR-Based RF Digital Twin Scene Construction for 28 GHz Indoor Ray-Tracing Channel Simulation |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2606.01261 |