Plenoptic Layer-Based Modeling for Image Based Rendering

Pearson, J.; Brookes, Mike; Dragotti, Pier Luigi

doi:10.1109/tip.2013.2268939

Cited by 42 publications

(23 citation statements)

References 86 publications

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“…reconstruction is to evaluate our filter skew η G rather than as a method for view synthesis in IBR. This is not to say that the proposed theory has no place in competitive IBR algorithms; for example, layer-based IBR algorithms, such as [16], [17], currently use the theory from [7] to inform the number of the layers in the scene.…”

Section: Simulation Resultsmentioning

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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Section: Simulation Resultsmentioning

confidence: 99%

“…In the context of IBR, it is worth noting that algorithms presented in [14], [15], [16], [17] partition the scene into a set of layers parallel to the cameras. This type of partition is equivalent to a piecewise constant approximation of scene depth.…”

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confidence: 99%

On the Spectrum of the Plenoptic Function

Gilliam

Dragotti

Brookes

2014

IEEE Trans. on Image Process.

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“…But, the displacement intra does not provide any scalability for transmission and rendering. In [31], a layered-based approach for light field images typically captured by camera arrays has been proposed. It explores the plenoptic sampling function, performs a nonuniformly spaced layer extraction, and conducts the rendering with a probabilistic interpolation approach.…”

Section: B Previous Workmentioning

confidence: 99%

Scalable Coding of Plenoptic Images by Using a Sparse Set and Disparities

Sjöström

Olsson

et al. 2016

IEEE Trans. on Image Process.

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This material is published in the open archive of Mid Sweden University DIVA http://miun.diva-portal.org to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders.All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. Abstract-One of the light field capturing techniques is the focused plenoptic capturing. By placing a microlens array in front of the photosensor, the focused plenoptic cameras capture both spatial and angular information of a scene in each microlens image and across microlens images. The capturing results in significant amount of redundant information, and the captured image is usually of a large resolution. A coding scheme that removes the redundancy before coding can be of advantage for efficient compression, transmission and rendering. In this paper, we propose a lossy coding scheme to efficiently represent plenoptic images. The format contains a sparse image set and its associated disparities. The reconstruction is performed by disparity-based interpolation and inpainting, and the reconstructed image is later employed as a prediction reference for the coding of the full plenoptic image. As an outcome of the representation, the proposed scheme inherits a scalable structure with three layers. The results show that plenoptic images are compressed efficiently with over 60 percent bit rate reduction compared to HEVC intra, and with over 20 percent compared to HEVC block copying mode.article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.

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“…), or have used a layer-based model for the scene of interest [15][16][17][18] . Obtaining the plenoptic function of a scene is difficult in practice and requires either an array of cameras or a multi lens camera 19 .…”

Section: 3mentioning

confidence: 99%

“…For example the problem of synthesising a novel view of a real-word scene given a set of images from different viewpoints, known as Image Based Rendering (IBR) [9][10][11][12][13][14][15] , can be seen as the problem of sampling and interpolating the plenoptic function. In order to perform this sampling and interpolation effectively many researchers have studied the spectral properties of the plenoptic function (e.g.…”

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Capturing the plenoptic function in a swipe

Lawson

Brookes

Dragotti

2016

Applications of Digital Image Processing XXXIX

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Blur in images, caused by camera motion, is typically thought of as a problem. The approach described in this paper shows instead that it is possible to use the blur caused by the integration of light rays at different positions along a moving camera trajectory to extract information about the light rays present within the scene. Retrieving the light rays of a scene from different viewpoints is equivalent to retrieving the plenoptic function of the scene. In this paper, we focus on a specific case in which the blurred image of a scene, containing a flat plane with a texture signal that is a sum of sine waves, is analysed to recreate the plenoptic function. The image is captured by a single lens camera with shutter open, moving in a straight line between two points, resulting in a swiped image. It is shown that finite rate of innovation sampling theory can be used to recover the scene geometry and therefore the epipolar plane image from the single swiped image. This epipolar plane image can be used to generate unblurred images for a given camera location.

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Plenoptic Layer-Based Modeling for Image Based Rendering

Cited by 42 publications

References 86 publications

On the Spectrum of the Plenoptic Function

On the Spectrum of the Plenoptic Function

Scalable Coding of Plenoptic Images by Using a Sparse Set and Disparities

Capturing the plenoptic function in a swipe

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