Plenoptic depth estimation from multiple aliased views

Bishop, Tom E.; Favaro, Paolo

doi:10.1109/iccvw.2009.5457420

Cited by 50 publications

(24 citation statements)

References 6 publications

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“…However, notice that as any rendering algorithm, constructing viewpoint images may produce other undesirable artifacts that will also affect the subjective quality of the visualized 3D object. In this case, the texture resampling from micro-images to the viewpoint images usually results in very low resolution images with severe aliasing artifacts [46].…”

Section: Test Conditionsmentioning

confidence: 99%

“…Angle-dependent PSNR: In this case the metric proposed in [45] is used, as given in (4) for all ܵ × ܶ viewpoint images of a 3D holoscopic image. Since it is possible to establish a relationship between the viewpoint image and the reconstructed scene that a user (who is at a particular viewing position) would see with a holoscopic display [46], this metric tries to directly analyze the quality of this visualized 3D object. However, notice that as any rendering algorithm, constructing viewpoint images may produce other undesirable artifacts that will also affect the subjective quality of the visualized 3D object.…”

Section: Test Conditionsmentioning

confidence: 99%

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HEVC-based 3D holoscopic video coding using self-similarity compensated prediction

Conti

Soares

Nunes

2016

Signal Processing: Image Communication

107

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Holoscopic imaging, also known as integral, light field, and plenoptic imaging, is an appealing technology for glassless 3D video systems, which has recently emerged as a prospective candidate for future image and video applications, such as 3D television. However, to successfully introduce 3D holoscopic video applications into the market, adequate coding tools that can efficiently handle 3D holoscopic video are necessary. In this context, this paper discusses the requirements and challenges for 3D holoscopic video coding, and presents an efficient 3D holoscopic coding scheme based on High Efficiency Video Coding (HEVC). The proposed 3D holoscopic codec makes use of the self-similarity (SS) compensated prediction concept to efficiently explore the inherent correlation of the 3D holoscopic content in Intraand Inter-coded frames, as well as a novel vector prediction scheme to take advantage of the peculiar characteristics of the SS prediction data. Extensive experiments were conducted, and have shown that the proposed solution is able to outperform HEVC as well as other coding solutions proposed in the literature. Moreover, a consistently better performance is also observed for a set of different quality metrics proposed in the literature for 3D holoscopic content, as well as for the visual quality of views synthesized from decompressed 3D holoscopic content.

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Section: Test Conditionsmentioning

confidence: 99%

Section: Test Conditionsmentioning

confidence: 99%

HEVC-based 3D holoscopic video coding using self-similarity compensated prediction

Conti

Soares

Nunes

2016

Signal Processing: Image Communication

107

View full text Add to dashboard Cite

show abstract

“…In this method a block taken from the center of the current micro-image for which disparity needs to be estimated is matched with only a fixed set of neighboring micro-images. Bishop and Favaro proposed a technique where disparity estimation and view generation was done in an iterative manner [9]. Wanner et al determine the disparity map based on gradient computation [10].…”

Section: Introductionmentioning

confidence: 99%

Subspace based disparity estimation for plenoptic cameras

Putraya¹,

Basavaraja²,

Uliyar³

et al. 2014

2014 IEEE International Conference on Image Processing (ICIP)

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We present a subspace based disparity estimation technique for plenoptic 2.0 lightfield cameras. The raw lightfield image contains a micro-image for every lens in the micro-lens array. The disparity of a scene point is typically estimated using multi-baseline approach. The multi-baseline approach necessitates that a focussed copy of a patch is present in at least one of the neighboring micro-images. This requirement limits the range over which the disparity can be reliably estimated. We propose a subspace based technique for disparity estimation wherein a subspace for every disparity is learnt separately, and the learnt subspaces are subsequently used for estimating the disparity of any micro-image. We estimate the disparities for the images captured using Raytrix R11 camera and compare the results with (a) estimates obtained from the multi-baseline approach, and (b) manufacturer provided disparity maps. Comparisons show that the disparity maps estimated by the proposed technique are superior. In addition, the proposed technique allows for extending the range over which the disparity can be estimated.

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“…A faster method of computing disparity, by trading off quality for better processing time is proposed in [6]. In [9], disparity is estimated by compensating for aliasing in the low resolution light field view images. A disparity estimation technique using the gradient features of the raw light field subimages is described in [10], which is an iterative method which generates microlens resolution disparity.…”

Section: Introductionmentioning

confidence: 99%

Pixel resolution plenoptic disparity using cost aggregation

Uliyar¹,

Putraya²,

Ukil³

et al. 2014

2014 IEEE International Conference on Image Processing (ICIP)

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We present a hierarchical method for estimating pixel resolution disparity from a raw Plenoptic 2.0 light field capture. Accurate pixel resolution disparity is essential for reconstruction of a high quality conventional image, and also for various applications that depend on disparity, like object segmentation, bokeh etc. Most light field disparity estimation methods in the literature compute disparity at microlens resolution, which is much lower than the resolution of the final reconstructed image. The algorithms that do compute pixel resolution disparity are iterative, making them computationally complex. The proposed method computes disparity hierarchically, in two steps. In the first step, microlens resolution disparity is computed, using which a conventional high resolution image is reconstructed. In the second step, globally smooth and accurate disparity is estimated at a pixel level on the reconstructed image, using the computationally efficient minimum spanning tree based cost aggregation approach. Experimental results demonstrate that the accuracy of the disparity maps generated by our method, in comparison to the Multibaseline and Raytrix algorithms is superior.

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Plenoptic depth estimation from multiple aliased views

Cited by 50 publications

References 6 publications

HEVC-based 3D holoscopic video coding using self-similarity compensated prediction

HEVC-based 3D holoscopic video coding using self-similarity compensated prediction

Subspace based disparity estimation for plenoptic cameras

Pixel resolution plenoptic disparity using cost aggregation

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