RF-MatID: Dataset and Benchmark for Radio Frequency Material Identification

2026-01-28

AI Summary

This work addresses the longstanding limitation in radio-frequency (RF) material identification caused by the absence of large-scale public datasets and systematic benchmarks. We present RF-MatID, the first open-source, large-scale, and wideband (4–43.5 GHz) dataset featuring geometrically diverse samples across 16 fine-grained classes and 5 superclasses, augmented with angular and distance perturbations to emulate real-world conditions. Built upon this dataset, we establish a comprehensive deep learning benchmark comprising 142,000 time–frequency domain samples under multiple protocols and experimental settings, enabling both band-level analysis and practical deployment. Extensive experiments evaluate state-of-the-art models in terms of in-distribution performance and out-of-distribution robustness, providing the community with a reproducible foundation for algorithm development and a standardized evaluation framework.

Technology Category

• Machine Learning: Matrix & Tensor Methods
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

Abstract

Accurate material identification plays a crucial role in embodied AI systems, enabling a wide range of applications. However, current vision-based solutions are limited by the inherent constraints of optical sensors, while radio-frequency (RF) approaches, which can reveal intrinsic material properties, have received growing attention. Despite this progress, RF-based material identification remains hindered by the lack of large-scale public datasets and the limited benchmarking of learning-based approaches. In this work, we present RF-MatID, the first open-source, large-scale, wide-band, and geometry-diverse RF dataset for fine-grained material identification. RF-MatID includes 16 fine-grained categories grouped into 5 superclasses, spanning a broad frequency range from 4 to 43.5 GHz, and comprises 142k samples in both frequency- and time-domain representations. The dataset systematically incorporates controlled geometry perturbations, including variations in incidence angle and stand-off distance. We further establish a multi-setting, multi-protocol benchmark by evaluating state-of-the-art deep learning models, assessing both in-distribution performance and out-of-distribution robustness under cross-angle and cross-distance shifts. The 5 frequency-allocation protocols enable systematic frequency- and region-level analysis, thereby facilitating real-world deployment. RF-MatID aims to enable reproducible research, accelerate algorithmic advancement, foster cross-domain robustness, and support the development of real-world application in RF-based material identification.

Problem

• RF-based material identification
• large-scale dataset
• benchmark
• out-of-distribution robustness
• frequency-domain analysis

Innovation

• RF-based material identification
• large-scale RF dataset
• geometry-diverse sensing
• out-of-distribution robustness
• frequency-domain benchmarking