Plenoptic spherical sampling

Bagnato, Luigi; Frossard, Pascal

doi:10.1109/icip.2012.6466869

Cited by 8 publications

(7 citation statements)

References 9 publications

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“…This unstructured lumigraph rendering technique directly renders views from an unstructured collection of input images. Similar results have been reported for dynamic light fields [23], geometrical plenoptic sampling [24], unstructured light fields [25], and plenoptic spherical sampling [26]. These methods can be applied to address light field rendering that was not restricted to a plane or to any specific manifold.…”

Section: A Light Field Samplingsupporting

confidence: 76%

A Light Field Sparse and Reconstruction Framework for Improving Rendering Quality

Zhang¹,

Zhu

Tang

et al. 2020

IEEE Access

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Section: A Light Field Samplingsupporting

confidence: 76%

A Light Field Sparse and Reconstruction Framework for Improving Rendering Quality

Zhang¹,

Zhu

Tang

et al. 2020

IEEE Access

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“…As we shall see in the next section, this new expression leads to a different Nyquist rate when compared to ∆t C from (11).…”

Section: Sampling and Reconstructionmentioning

confidence: 93%

“…The results of the analysis 7 To calculate N C , we substitute the definition of Ωv from (30) into the camera spacing defined in (11). are shown in Fig.…”

Section: A Synthetic Scenesmentioning

confidence: 99%

“…More recently the focus of plenoptic sampling has been on the acquisition set-up. For instance, cameras with arbitrary position and orientation are considered in [10], and Bagnato et al [11] examined plenoptic sampling when the cameras are placed on a sphere (as opposed to a plane in [7], [8], [9], [10]). A detailed review of plenoptic sampling, including non-uniform camera placement, was presented by Zhang and Chen in [12].…”

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confidence: 99%

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On the Spectrum of the Plenoptic Function

Gilliam

Dragotti

Brookes

2014

IEEE Trans. on Image Process.

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Abstract-The plenoptic function is a powerful tool to analyze the properties of multi-view image datasets. In particular, the understanding of the spectral properties of the plenoptic function is essential in many computer vision applications including image-based rendering. In this paper, we derive for the first time an exact closed-form expression of the plenoptic spectrum of a slanted plane with finite width and use this expression as the elementary building block to derive the plenoptic spectrum of more sophisticated scenes. This is achieved by approximating the geometry of the scene with a set of slanted planes and evaluating the closed-form expression for each plane in the set. We then use this closed-form expression to revisit uniform plenoptic sampling. In this context, we derive a new Nyquist rate for the plenoptic sampling of a slanted plane and a new reconstruction filter. Through numerical simulations, on both real and synthetic scenes, we show that the new filter outperforms alternative existing filters.

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“…Chen et al [10] examined the plenoptic spectrum of unstructured camera systems. Bagnato et al [11] analyzed the plenoptic sampling when the cameras are arranged on a sphere.…”

Section: Introductionmentioning

confidence: 99%

The plenoptic spectrum analysis and reconstruction filter design for the occlusive scene

Zhou

Zhong

2016

Neurocomputing

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The spectral analysis of the plenoptic function is an intuitive way to acquire the sampling rate of the image-based rendering (IBR) data. In this paper, we present a new epipolar plane image (EPI) model to analyze the spectrum of the plenoptic function and derive the plenoptic spectrum of the occlusive scene. In particular, the spectrum is separated into two aspects. The main part of the spectrum is bounded by the minimum and maximum depths of the scene. The second part is the broadening area which is analyzed by modeling the EPI lines. Our analysis reveals that, the occlusion leads to the spectrum broadening, and the broadening range depends on the occluding depth difference, the size of the occluding object and the maximum frequency of the texture painted on the occluding object. Based on the spectrum analysis, a new Nyquist rate and the corresponding reconstruction filter are deduced for the occlusive scene. Experiments on both synthetic scenes and real scenes show that our sampling spacing and reconstruction filter outperform other methods.

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Plenoptic spherical sampling

Cited by 8 publications

References 9 publications

A Light Field Sparse and Reconstruction Framework for Improving Rendering Quality

A Light Field Sparse and Reconstruction Framework for Improving Rendering Quality

On the Spectrum of the Plenoptic Function

The plenoptic spectrum analysis and reconstruction filter design for the occlusive scene

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