SynthRM: A Synthetic Data Platform for Vision-Aided Mobile System Simulation
Yingzhe Mao, Chao Zou, Yanqun Tang

TL;DR
SynthRM is a scalable synthetic data platform that aligns visual and electromagnetic data for improved urban wireless sensing, enabling more accurate and physically consistent modeling of signal behavior in complex environments.
Contribution
It introduces a novel Visible-Aligned-Surface simulation strategy that ensures pixel-level consistency between visual data and electromagnetic responses, addressing geometric ambiguities in urban sensing.
Findings
Provides a city-scale dataset from procedurally generated environments.
Demonstrates improved physical consistency in visual-electromagnetic data alignment.
Enables development of environment-aware sensing and communication systems.
Abstract
Vision-aided wireless sensing is emerging as a cornerstone of 6G mobile computing. While data-driven approaches have advanced rapidly, establishing a precise geometric correspondence between ego-centric visual data and radio propagation remains a challenge. Existing paradigms typically either associate 2D topology maps and auxiliary information with radio maps, or provide 3D perspective views limited by sparse radio data. This spatial representation flattens the complex vertical interactions such as occlusion and diffraction that govern signal behavior in urban environments, rendering the task of cross-view signal inference mathematically ill-posed. To resolve this geometric ambiguity, we introduce SynthRM, a scalable synthetic data platform. SynthRM implements a Visible-Aligned-Surface simulation strategy: rather than probing a global volumetric grid, it performs ray-tracing directly…
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Taxonomy
TopicsAdvanced Wireless Communication Technologies · Augmented Reality Applications · 3D Modeling in Geospatial Applications
