Polarization 3D imaging technology: a review

Li, Xuan; Liu, Zhiqiang; Cai, Yici; Chen, Pan; Song, Jiawei; Wang, Jinshou; Shao, Xiaopeng

doi:10.3389/fphy.2023.1198457

Cited by 7 publications

(3 citation statements)

References 105 publications

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“…To conclude our overview of the state of the art, we want to mention that there are special 3D sensors that use multimodal cameras to obtain 3D shape data in the first place where established techniques fail, e.g., for very shiny [62][63][64] or transparent [65] objects or for single-shot scenes [66].…”

Section: State Of the Artmentioning

confidence: 99%

Fusion of Multimodal Imaging and 3D Digitization Using Photogrammetry

Ramm,

de Dios Cruz,

Heist

et al. 2024

Sensors

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Multimodal sensors capture and integrate diverse characteristics of a scene to maximize information gain. In optics, this may involve capturing intensity in specific spectra or polarization states to determine factors such as material properties or an individual’s health conditions. Combining multimodal camera data with shape data from 3D sensors is a challenging issue. Multimodal cameras, e.g., hyperspectral cameras, or cameras outside the visible light spectrum, e.g., thermal cameras, lack strongly in terms of resolution and image quality compared with state-of-the-art photo cameras. In this article, a new method is demonstrated to superimpose multimodal image data onto a 3D model created by multi-view photogrammetry. While a high-resolution photo camera captures a set of images from varying view angles to reconstruct a detailed 3D model of the scene, low-resolution multimodal camera(s) simultaneously record the scene. All cameras are pre-calibrated and rigidly mounted on a rig, i.e., their imaging properties and relative positions are known. The method was realized in a laboratory setup consisting of a professional photo camera, a thermal camera, and a 12-channel multispectral camera. In our experiments, an accuracy better than one pixel was achieved for the data fusion using multimodal superimposition. Finally, application examples of multimodal 3D digitization are demonstrated, and further steps to system realization are discussed.

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Section: State Of the Artmentioning

confidence: 99%

Fusion of Multimodal Imaging and 3D Digitization Using Photogrammetry

Ramm,

de Dios Cruz,

Heist

et al. 2024

Sensors

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show abstract

“…Polarization images are not only intuitively visible but also contribute to scene analysis, bolstering target detection capabilities [14,15]. Specific photoreceptors dedicated to polarized light vision play a crucial role in numerous fields, including the separation of specular and diffuse reflections [16,17], material classification [18,19], three-dimensional reconstruction [20,21], anomaly detection [22,23], and the separation of man-made and camouflaged objects [24,25].…”

Section: Introductionmentioning

confidence: 99%

Multi-Dimensional Fusion of Spectral and Polarimetric Images Followed by Pseudo-Color Algorithm Integration and Mapping in HSI Space

Guo,

Zhu,

Huang

et al. 2024

Remote Sensing

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Spectral–polarization imaging technology plays a crucial role in remote sensing detection, enhancing target identification and tracking capabilities by capturing both spectral and polarization information reflected from object surfaces. However, the acquisition of multi-dimensional data often leads to extensive datasets that necessitate comprehensive analysis, thereby impeding the convenience and efficiency of remote sensing detection. To address this challenge, we propose a fusion algorithm based on spectral–polarization characteristics, incorporating principal component analysis (PCA) and energy weighting. This algorithm effectively consolidates multi-dimensional features within the scene into a single image, enhancing object details and enriching edge features. The robustness and universality of our proposed algorithm are demonstrated through experimentally obtained datasets and verified with publicly available datasets. Additionally, to meet the requirements of remote sensing tracking, we meticulously designed a pseudo-color mapping scheme consistent with human vision. This scheme maps polarization degree to color saturation, polarization angle to hue, and the fused image to intensity, resulting in a visual display aligned with human visual perception. We also discuss the application of this technique in processing data generated by the Channel-modulated static birefringent Fourier transform imaging spectropolarimeter (CSBFTIS). Experimental results demonstrate a significant enhancement in the information entropy and average gradient of the fused image compared to the optimal image before fusion, achieving maximum increases of 88% and 94%, respectively. This provides a solid foundation for target recognition and tracking in airborne remote sensing detection.

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“…The inherent trade-off between angular and spatial resolution remains unresolved without the introduction of additional information or techniques [10][11][12] . As another dimension of light field information, polarization has demonstrated its potential for integration with other 3D imaging techniques in terms of obtaining finer 3D reconstructions results [13][14][15][16] . Unfortunately, this aspect has received limited attention and is ignored in LFM.…”

Section: 、Introductionmentioning

confidence: 99%

Resolution enhancement for light field microscopic 3D imaging based on polarization and data fusion

Shi,

Kong

2023

AOPC 2023: Computing Imaging Technology

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Light field microscopy (LFM) is an emerging three-dimensional (3D) imaging technology that simultaneously captures both spatial and angular information of the incident light by placing a micro lens array (MLA) in front of an imaging sensor, enabling computational reconstruction of the full 3D volume of a specimen via a single camera frame or single shot imaging. Unlike other 3D imaging techniques that acquire spatial information sequentially or through scanning, LFM four-dimensional (4D) imaging scheme effectively frees volume acquisition time from spatial scales and easy to miniaturize, thus making LFM a highly scalable tool in diverse applications. However, its broad application has slowed down due to the low resolution of the limited angular and spatial information in one snapshot of captured image, the inhomogeneous resolution of reconstructed depth images, and the lack of lateral shift invariance, which greatly degrades the spatial resolution, causing grid-like artifacts and great computational complexity. The introduction of Fourier light field microscopy (FLFM) provides a promising path to improve the current LFM techniques and achieve high-quality imaging and rapid light field reconstruction. However, the inherent trade-off between the angular and spatial resolution is still not fundamentally resolved without the introduction of additional information. Polarization, another dimension of light field information, has been shown to integrate well with other 3D imaging techniques to obtain finer 3D reconstructions results. Unfortunately, this aspect has seldom received attention and is ignored in LFM.This paper presents a resolution enhancement scheme for Fourier light field microscopy system that utilizes polarization norms and light field point cloud data fusion to generate improved imaging resolution and 3D reconstruction accuracy. Different from conventional FLFM, this approach actively introduces additional surface polarization information of the sample into the reconstruction of 3D volume information. A universal polarization-integrated FLFM configuration is designed and built up, allowing polarization and light field data to be acquired simultaneously using the same set of optical paths. A mathematical model is derived to describe the mapping and fusion of the polarization norms and light field point cloud data. Simulation studies show that the resolution and accuracy of the 3D reconstruction of the proposed FLFM imaging system are significantly improved after incorporating the polarization information, confirming the validity of the proposed methods. Finally, the implications of this approach for FLM are discussed, providing guidance for future experiments and applications. The resolution enhancement approach based on polarization and data fusion provides a feasible solution to the contradiction between lateral resolution and vertical resolution, and further improve the resolution of the FLFM imaging system.

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Polarization 3D imaging technology: a review

Cited by 7 publications

References 105 publications

Fusion of Multimodal Imaging and 3D Digitization Using Photogrammetry

Fusion of Multimodal Imaging and 3D Digitization Using Photogrammetry

Multi-Dimensional Fusion of Spectral and Polarimetric Images Followed by Pseudo-Color Algorithm Integration and Mapping in HSI Space

Resolution enhancement for light field microscopic 3D imaging based on polarization and data fusion

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