In this paper, we describe how scene depth can be extracted using a hyperspectral light field capture (H-LF) system. Our H-LF system consists of a $5 \times 6$5×6 array of cameras, with each camera sampling a different narrow band in the visible spectrum. There are two parts to extracting scene depth. The first part is our novel cross-spectral pairwise matching technique, which involves a new spectral-invariant feature descriptor and its companion matching metric we call bidirectional weighted normalized cross correlation (BWNCC). The second part, namely, H-LF stereo matching, uses a combination of spectral-dependent correspondence and defocus cues. These two new cost terms are integrated into a Markov Random Field (MRF) for disparity estimation. Experiments on synthetic and real H-LF data show that our approach can produce high-quality disparity maps. We also show that these results can be used to produce the complete plenoptic cube in addition to synthesizing all-focus and defocused color images under different sensor spectral responses.

中文翻译：

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高光谱光场立体匹配

在本文中，我们描述了如何使用高光谱光场捕获（H-LF）系统提取场景深度。我们的H-LF系统包括5美元/ 6次5×6摄像机阵列，每个摄像机在可见光谱中采样一个不同的窄带。提取场景深度分为两个部分。第一部分是我们新颖的互谱成对匹配技术，其中涉及一个新的谱不变特征描述符及其伴随匹配度量，我们称之为双向加权归一化互相关（BWNCC）。第二部分，即H-LF立体声匹配，结合了频谱相关的对应关系和散焦提示。将这两个新的成本项集成到马尔可夫随机场（MRF）中以进行视差估计。对合成和实际H-LF数据进行的实验表明，我们的方法可以生成高质量的视差图。