MAROON: A Dataset for the Joint Characterization of Near-Field High-Resolution Radio-Frequency and Optical Depth Imaging Techniques
Vanessa Wirth, Johanna Br\"aunig, Nikolai Hofmann, Martin Vossiek, Tim Weyrich, Marc Stamminger

TL;DR
This paper introduces MAROON, a multimodal dataset that jointly characterizes near-field optical and radio-frequency depth sensors, revealing their behaviors across various materials and geometries for improved sensor fusion in close-range applications.
Contribution
It provides the first comprehensive dataset and analysis comparing optical and radio-frequency depth sensors in near-field conditions, including calibration and material effects.
Findings
Radio-frequency signals respond differently to materials compared to optical sensors.
Scattering effects vary significantly with object material and geometry.
The dataset enables future research in multimodal depth sensing and sensor fusion.
Abstract
Utilizing the complementary strengths of wavelength-specific range or depth sensors is crucial for robust computer-assisted tasks such as autonomous driving. Despite this, there is still little research done at the intersection of optical depth sensors and radars operating close range, where the target is decimeters away from the sensors. Together with a growing interest in high-resolution imaging radars operating in the near field, the question arises how these sensors behave in comparison to their traditional optical counterparts. In this work, we take on the unique challenge of jointly characterizing depth imagers from both, the optical and radio-frequency domain using a multimodal spatial calibration. We collect data from four depth imagers, with three optical sensors of varying operation principle and an imaging radar. We provide a comprehensive evaluation of their depth…
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Taxonomy
TopicsIntegrated Circuits and Semiconductor Failure Analysis · Advanced Optical Sensing Technologies · Advanced SAR Imaging Techniques
