Passive 3D Scene Reconstruction via Hyperspectral Imagery

Miller, Corey; Walls, Thomas J.

doi:10.1007/978-3-319-14249-4_39

Cited by 4 publications

(4 citation statements)

References 10 publications

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“…There also exist some systems for spectral-3D model acquisition. One intuitive approach is to apply an SfM and MVS pipeline using multiview multispectral images to generate a point cloud with spectral information [6], [24], [28], [37]. Another approach is to combine multispectral imaging and photometric stereo to estimate dense surface normals and the spectral reflectance using the images captured under different light source positions [26], [27], [38].…”

Section: Spectral-3d Acquisition Systemsmentioning

confidence: 99%

“…Active 3D scanning, such as a laser scanner [25], a ToF camera [39], and structured light [23], [40], is also combined with multispectral imaging for obtaining a high-quality 3D model with spectral information. However, the above-mentioned systems rely on expensive special equipment, such as a multispectral camera [6], [25], [27], [28], [37], [38], [40] and a multispectral light source [23], [24], [26], or require dedicated geometric calibration for each considered system [23], [25], [38], [39], [40].…”

Section: Spectral-3d Acquisition Systemsmentioning

confidence: 99%

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Spectral MVIR: Joint Reconstruction of 3D Shape and Spectral Reflectance

Monno²,

Okutomi³

2021

Preprint

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Reconstructing an object's high-quality 3D shape with inherent spectral reflectance property, beyond typical device-dependent RGB albedos, opens the door to applications requiring a high-fidelity 3D model in terms of both geometry and photometry. In this paper, we propose a novel Multi-View Inverse Rendering (MVIR) method called Spectral MVIR for jointly reconstructing the 3D shape and the spectral reflectance for each point of object surfaces from multi-view images captured using a standard RGB camera and low-cost lighting equipment such as an LED bulb or an LED projector. Our main contributions are twofold: (i) We present a rendering model that considers both geometric and photometric principles in the image formation by explicitly considering camera spectral sensitivity, light's spectral power distribution, and light source positions. (ii) Based on the derived model, we build a cost-optimization MVIR framework for the joint reconstruction of the 3D shape and the per-vertex spectral reflectance while estimating the light source positions and the shadows. Different from most existing spectral-3D acquisition methods, our method does not require expensive special equipment and cumbersome geometric calibration. Experimental results using both synthetic and real-world data demonstrate that our Spectral MVIR can acquire a high-quality 3D model with accurate spectral reflectance property.

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Section: Spectral-3d Acquisition Systemsmentioning

confidence: 99%

Section: Spectral-3d Acquisition Systemsmentioning

confidence: 99%

Spectral MVIR: Joint Reconstruction of 3D Shape and Spectral Reflectance

Monno²,

Okutomi³

2021

Preprint

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“…Previous studies found that adding spectral data from only six channels of a hyperspectral scene can increase the number of correspondence points over standard images by a factor of more than six. 3 Higher density point clouds improve the accuracy and precision of geometric measurements extracted from the mesh reconstructions. 10…”

Section: Occlusion Removal and Resolution Enhancementmentioning

confidence: 99%

“…However, the workflows to reconstruct 3D models seamlessly and automatically from hyperspectral images are still developing. 3 The organization of this paper begins by reviewing the fundamentals of SfM (section 2.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral range imaging for transportation systems evaluation

et al. 2016

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Transportation agencies expend significant resources to inspect critical infrastructure such as roadways, railways, and pipelines. Regular inspections identify important defects and generate data to forecast maintenance needs. However, cost and practical limitations prevent the scaling of current inspection methods beyond relatively small portions of the network. Consequently, existing approaches fail to discover many high-risk defect formations. Remote sensing techniques offer the potential for more rapid and extensive non-destructive evaluations of the multimodal transportation infrastructure. However, optical occlusions and limitations in the spatial resolution of typical airborne and spaceborne platforms limit their applicability. This research proposes hyperspectral image classification to isolate transportation infrastructure targets for high-resolution photogrammetric analysis. A plenoptic swarm of unmanned aircraft systems will capture images with centimeter-scale spatial resolution, large swaths, and polarization diversity. The light field solution will incorporate structure-from-motion techniques to reconstruct three-dimensional details of the isolated targets from sequences of two-dimensional images. A comparative analysis of existing low-power wireless communications standards suggests an application dependent tradeoff in selecting the best-suited link to coordinate swarming operations. This study further produced a taxonomy of specific roadway and railway defects, distress symptoms, and other anomalies that the proposed plenoptic swarm sensing system would identify and characterize to estimate risk levels.

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Hyperspectral Scene Analysis via Structure from Motion

Miller

Walls

2015

Advances in Visual Computing

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Passive 3D Scene Reconstruction via Hyperspectral Imagery

Cited by 4 publications

References 10 publications

Spectral MVIR: Joint Reconstruction of 3D Shape and Spectral Reflectance

Spectral MVIR: Joint Reconstruction of 3D Shape and Spectral Reflectance

Hyperspectral range imaging for transportation systems evaluation

Hyperspectral Scene Analysis via Structure from Motion

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