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SynthSoM: A synthetic intelligent multi-modal sensing-communication dataset for Synesthesia of Machines (SoM)
📌 Overview
SynthSoM dataset covers eight rich and diverse application scenarios, including vehicle-road coordination, low-altitude economy, smart campus, as well as typical urban, suburban, rural, and campus environments. The urban scenario further includes intersections, ultra-wide lanes, elevated interchanges, and CBD areas; the suburban scenario includes forked intersections; the rural scenario covers mountainous roads; and the campus scenario includes Peking University’s Yan Garden and New Yan Garden. Meanwhile, the data collection and construction process comprehensively considers six categories of scene conditions: multiple frequency bands, multiple antenna arrays, multiple time periods, multiple weather conditions, multiple viewpoints, and multiple traffic density levels.
The constructed SynthSoM dataset contains more than 9.5 million groups of multimodal data, with a total data volume exceeding 10 TB. Specifically, it includes 1.7 million RGB images, 3.4 million depth maps, 1.5 million LiDAR point clouds, 750,000 radar waveforms, 1.5 million channel matrices, and 800,000 path loss records. Overall, its scale is more than five times larger than existing multimodal integrated sensing-and-communication datasets.
📜 Related paper
X. Cheng, Z. Huang, Y. Yu, L. Bai, M. Sun, Z. Han, R. Zhang, and S. Li, “SynthSoM: A synthetic intelligent multi-modal sensing-communication dataset for Synesthesia of Machines (SoM),” Scientific Data, vol. 12, pp. 819-833, May 2025.
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