Full-parallax 3D light field display with uniform view density along the horizontal and vertical direction

Gao, Xin; Sang, Xinzhu; Xing, Shujun; Yu, Xunbo; Yan, Binbin; Liu, Boyang; Wang, Peiren

doi:10.1016/j.optcom.2020.125765

Cited by 7 publications

(2 citation statements)

References 17 publications

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“…The visualization techniques and output devices may allow an extended depth of field, refocusing, and 3D views. Horizontal-parallax-only and vertical-parallax-only displays limit the practical application of 3D display technology and the assessment of the discomfort issue [30][31][32][33].…”

Section: Light Field Visualizationmentioning

confidence: 99%

Learning-based light field imaging: an overview

Mahmoudpour,

Pagliari,

Schelkens

2024

J Image Video Proc.

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Conventional photography can only provide a two-dimensional image of the scene, whereas emerging imaging modalities such as light field enable the representation of higher dimensional visual information by capturing light rays from different directions. Light fields provide immersive experiences, a sense of presence in the scene, and can enhance different vision tasks. Hence, research into light field processing methods has become increasingly popular. It does, however, come at the cost of higher data volume and computational complexity. With the growing deployment of machine-learning and deep architectures in image processing applications, a paradigm shift toward learning-based approaches has also been observed in the design of light field processing methods. Various learning-based approaches are developed to process the high volume of light field data efficiently for different vision tasks while improving performance. Taking into account the diversity of light field vision tasks and the deployed learning-based frameworks, it is necessary to survey the scattered learning-based works in the domain to gain insight into the current trends and challenges. This paper aims to review the existing learning-based solutions for light field imaging and to summarize the most promising frameworks. Moreover, evaluation methods and available light field datasets are highlighted. Lastly, the review concludes with a brief outlook for future research directions.

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Section: Light Field Visualizationmentioning

confidence: 99%

Learning-based light field imaging: an overview

Mahmoudpour,

Pagliari,

Schelkens

2024

J Image Video Proc.

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“…Such technology is somewhat meant to deliver the "ultimate" glasses-free visualization-particularly with a high ray density-since the correct parallax effect may be provided along both the horizontal and the vertical axis. However, while progress is indeed notable [66,67], similarly to super resolution, the emergence of FP displays is yet to come.…”

Section: Full-parallax Imagingmentioning

confidence: 99%

Beyond Perceptual Thresholds and Personal Preference: Towards Novel Research Questions and Methodologies of Quality of Experience Studies on Light Field Visualization

et al. 2022

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With the upcoming emergence of the IEEE P3333.1.4 standard, the first ever standardization document on the subjective quality assessment of light field visualization, we are approaching a major milestone of light field Quality of Experience (QoE). The research efforts leading up to this point have answered the fundamental questions regarding perceptual thresholds and personal preference, and best practices regarding research methodologies have been formed. However, in order to introduce successful and efficient light field systems and services, research questions beyond perceptual thresholds and personal preference must be addressed, along with the associated methodologies. In this paper, we propose a comprehensive set of novel research questions and methodologies regarding light field QoE. Our work also provides a detailed discussion of related factors, particularly those that extend to test participants and thus may affect the results of subjective studies.

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