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arXiv 提交日期: 2026-05-25
📄 Abstract - Broadband Hyperspectral 3D Imaging using Dispersed Structured Light

Hyperspectral 3D imaging enables the capture of dense spectral information and scene geometry but has traditionally been confined to narrow spectral windows, typically the visible range. In this work, we introduce a broadband hyperspectral 3D imaging (BH3D) method to extend this capability across the full visible-near-infrared and short-wavelength infrared (SWIR) spectrum (450-1500 nm). This broad coverage is critical as it captures complementary physical cues: visible wavelengths reveal surface appearance, while SWIR bands provide insight into subsurface properties and material composition. However, realizing BH3D is challenging due to fundamental sensor constraints between visible-spectrum silicon and SWIR-spectrum InGaAs sensors, which necessitate complex multi-spectrograph designs. Here we propose a single-spectrograph BH3D system, using a stereo setup comprising visible and SWIR cameras, that reconstructs dense broadband hyperspectral reflectance together with accurate 3D geometry. Our key idea is to extend dispersed structured light to the broadband regime using a single spectrograph. We model the image formation of broadband dispersed structured light, and estimate hyperspectral reflectance and depth. We validate our approach on diverse real-world scenes, demonstrating accurate reconstruction with a mean spectral angle mapper of 0.13 rad, root mean square error of 0.03, and mean depth error of 4.5 mm. We further demonstrate identifying metameric materials, performing imaging through opaque layers, uncovering hidden features on banknotes, and revealing blood vessels.

顶级标签: computer vision systems
详细标签: hyperspectral imaging 3d reconstruction structured light swir multi-spectral 或 搜索:

宽带高光谱三维成像:基于色散结构光的方法 / Broadband Hyperspectral 3D Imaging using Dispersed Structured Light


1️⃣ 一句话总结

本研究提出一种名为BH3D的新型成像技术,通过单个光谱仪和可见光-红外双摄像头组合,首次实现了从可见光到短波红外(450-1500纳米)的宽带高光谱三维成像,能同时获取物体的精细光谱信息和立体形状,并应用于识别伪钞、透视遮挡层和检测隐藏特征等场景。

源自 arXiv: 2605.25757