Light field calibration and 3D shape measurement based on epipolar-space

Zhou, Ping; Yang, Zi; Cai, Weijia; Yu, Yunlei; Zhou, Guangquan

doi:10.1364/oe.27.010171

Cited by 21 publications

(4 citation statements)

References 11 publications

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“…Monteiro et al [40] further refined the calibration model, addressing limitations such as the complete viewpoint projection matrix for the definition and consistent lens parameters. In the case of the epipolar-plane-image (EPI)-based recovery method, Zhou et al [41] proposed a geometric model known as the epipolar space (EPS), which directly determines the relationship between the 3D points to be measured and the corresponding parameter vectors in the polar space through calibration. The study of light field cameras extends beyond the aforementioned results, with numerous studies and innovations focusing on parameter separation, adaptive calibration without calibration plates, and projection assistance, among others.…”

Section: Calibration Technology Of the Light Field Cameramentioning

confidence: 99%

Section: Calibration Technology Of the Light Field Cameramentioning

confidence: 99%

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Measurement Technologies of Light Field Camera: An Overview

Hu,

Li,

Miao

et al. 2023

Sensors

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Visual measurement methods are extensively used in various fields, such as aerospace, biomedicine, agricultural production, and social life, owing to their advantages of high speed, high accuracy, and non-contact. However, traditional camera-based measurement systems, relying on the pinhole imaging model, face challenges in achieving three-dimensional measurements using a single camera by one shot. Moreover, traditional visual systems struggle to meet the requirements of high precision, efficiency, and compact size simultaneously. With the development of light field theory, the light field camera has garnered significant attention as a novel measurement method. Due to its special structure, the light field camera enables high-precision three-dimensional measurements with a single camera through only one shot. This paper presents a comprehensive overview of light field camera measurement technologies, including the imaging principles, calibration methods, reconstruction algorithms, and measurement applications. Additionally, we explored future research directions and the potential application prospects of the light field camera.

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Section: Calibration Technology Of the Light Field Cameramentioning

confidence: 99%

Section: Calibration Technology Of the Light Field Cameramentioning

confidence: 99%

Measurement Technologies of Light Field Camera: An Overview

Hu,

Li,

Miao

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…An approach by Sun et al [Sun19a] works with three calibration targets at different distances together with gradient-based correspondences search. In the publication of Zhou et al [Zho19b], the calibration problem is solved by estimating the original homography solution using a calibration scheme and its subsequent nonlinear optimization. Ji and Wu in [Ji19c] propose a calibration model for plenoptic cameras calibration with a conventional calibration target.…”

Section: Related Workmentioning

confidence: 99%

Calibration and Auto-Refinement for Light Field Cameras

Anisimov¹,

Reis²,

Stricker³

2021

CSRN

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The ability to create an accurate three-dimensional reconstruction of a captured scene draws attention to the prin- ciples of light fields. This paper presents an approach for light field camera calibration and rectification, based on pairwise pattern-based parameters extraction. It is followed by a correspondence-based algorithm for camera parameters refinement from arbitrary scenes using the triangulation filter and nonlinear optimization. The effec- tiveness of our approach is validated on both real and synthetic data.

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“…To further improve measurement performance, disparity patterns [8] based on the parallax information of the recorded data were presented to enhance the accuracy of depth estimation. Methods based on epipolar space [9], point spread function [10], and structured light [11] were proposed for a more accurate depth reconstruction from the measurement data. Research on the elimination of specular light [12] and enhancement of data resolution [13] was performed to strengthen the quality of acquired data.…”

Section: Introductionmentioning

confidence: 99%

Autostereoscopic 3D Measurement Based on Adaptive Focus Volume Aggregation

Gao,

Cheung

2023

Sensors

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Autostereoscopic three-dimensional measuring systems are a kind of portable and fast precision metrology instrument. The systems are based on integral imaging that makes use of a micro-lens array before an image sensor to observe measured parts from multiple perspectives. Since autostereoscopic measuring systems can obtain longitudinal and lateral information within single snapshots rapidly, the three-dimensional profiles of the measured parts can be reconstructed by shape from focus. In general, the reconstruction process consists of data acquisition, pre-processing, digital refocusing, focus measures, and depth estimation. The accuracy of depth estimation is determined by the focus volume generated by focus measure operators which could be sensitive to the noise during digital refocusing. Without prior knowledge and surface information, directly estimated depth maps usually contain severe noise and incorrect representation of continuous surfaces. To eliminate the effects of refocusing noise and take advantage of traditional focus measure methods with robustness, an adaptive focus volume aggregation method based on convolutional neural networks is presented to optimize the focus volume for more accurate depth estimation. Since a large amount of data and ground truth are costly to acquire for model convergence, backpropagation is performed for every sample under an unsupervised strategy. The training strategy makes use of a smoothness constraint and an identical distribution constraint that restricts the difference between the distribution of the network output and the distribution of ideal depth estimation. Experimental results show that the proposed adaptive aggregation method significantly reduces the noise during depth estimation and retains more accurate surface profiles. As a result, the autostereoscopic measuring system can directly recover surface profiles from raw data without any prior information.

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Light field calibration and 3D shape measurement based on epipolar-space

Cited by 21 publications

References 11 publications

Measurement Technologies of Light Field Camera: An Overview

Measurement Technologies of Light Field Camera: An Overview

Calibration and Auto-Refinement for Light Field Cameras

Autostereoscopic 3D Measurement Based on Adaptive Focus Volume Aggregation

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