Inverse Rendering of Near-Field mmWave MIMO Radar for Material Reconstruction

IF 6.9 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Nikolai Hofmann;Vanessa Wirth;Johanna Bräunig;Ingrid Ullmann;Martin Vossiek;Tim Weyrich;Marc Stamminger
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引用次数: 0

Abstract

Near-field multiple-input multiple-output (MIMO) radar systems allow for high-resolution spatial imaging by leveraging multiple antennas to transmit and receive signals across multiple perspectives. This capability is particularly advantageous in challenging environments, where optical imaging techniques struggle. We present a novel approach to inverse rendering for near-field MIMO radar systems, aimed at reconstructing material properties such as surface roughness, dielectric constants, and conductivity from radar and ground-truth mesh data, for example obtained from multi-view stereo. Drawing inspiration from physically based rendering techniques in computer graphics, we formalize an advanced inverse rendering algorithm that integrates electromagnetic wave propagation models directly into the optimization process. To avoid bias from conventional radar image reconstruction algorithms in the optimization process, we directly derive gradients from raw radar outputs, resulting in more accurate material characterization. We validate our approach through extensive experiments on both synthetic and real radar datasets, demonstrating its effectiveness in a multitude of scenarios.
面向材料重构的近场毫米波MIMO雷达逆绘制
近场多输入多输出(MIMO)雷达系统通过利用多个天线跨多个角度发送和接收信号,实现高分辨率空间成像。这种能力在具有挑战性的环境中尤其具有优势,在这些环境中,光学成像技术很困难。我们提出了一种用于近场MIMO雷达系统的反向渲染的新方法,旨在从雷达和地面真实网格数据中重建材料属性,如表面粗糙度、介电常数和电导率,例如从多视图立体图像中获得的数据。从计算机图形学中基于物理的渲染技术中汲取灵感,我们形式化了一种先进的反向渲染算法,该算法将电磁波传播模型直接集成到优化过程中。为了避免传统雷达图像重建算法在优化过程中的偏差,我们直接从原始雷达输出中导出梯度,从而获得更准确的材料表征。我们通过在合成和真实雷达数据集上的大量实验验证了我们的方法,证明了它在多种场景下的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
10.70
自引率
0.00%
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0
审稿时长
8 weeks
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