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The light field (LF) imaging technique can capture 3D scene information in 4D by recording both 2D intensity and 2D direction of incoming light rays. Due to this capability, LF has shown a great interest in virtual reality (VR) and augmented reality (AR) for enhanced immersion, improved depth perception and reconstruction of realistic 3D environments. This paper presents a comprehensive review of LF imaging technology and other approaches used for VR content creation. The applications of LF technology beyond VR and AR are also discussed. The challenges and limitations of other approaches for VR content creation are examined. State‐of‐the‐art research has focused on how VR experiences benefit from LF technology and identified the challenges to creating comfortable, immersive and realistic VR content such as (1) image size and resolution, (2) processing speed, (3) precise calibration and (4) depth reconstruction. Recommendations that can be considered for creating immersive VR content are provided to enhance user experience. These recommendations aim to contribute to developing more comfortable and realistic VR content, extending the potential applications of LF imaging technology in diverse fields. [ABSTRACT FROM AUTHOR]

Tomographic reconstruction of three-dimensional (3D) tracer particle distributions through light field particle image velocimetry (LF-PIV) faces challenges in low reconstruction resolution owing to the elongation effect and extensive computational cost incurred by the iterative process. To resolve these challenges, this study proposes a deep neural network-based volumetric reconstruction approach to alleviate the reconstruction elongation and enhance the reconstruction efficiency. A tailored deep learning model (namely, LF-DNN) incorporating residual neural network architecture and a novel hybrid loss function is established to reconstruct the particle distributions through LF images. The parallax information of the flow field decoded from the raw LF data is leveraged as the input features of the network model. Comparative studies between the proposed method and the traditional tomographic reconstruction algorithms (multiplicative algebraic reconstruction technique, MART and pre-recognition MART, PR-MART) are performed through synthetic datasets. Experiments on a cylinder wake flow are further conducted to validate the performance of the proposed LF-DNN. The results indicate that the LF-DNN outperforms MART and PR-MART in terms of the reconstruction quality, mitigation of elongation effect, and noise resilience. The LF-DNN also improves the reconstruction efficiency which is 9.6 and 7.1 times higher than the MART and PR-MART, respectively. The relative error of the cylinder wake flow achieved by the LF-DNN is 2% lower than the MART. It suggests that the LF-DNN can facilitate accurate volumetric particle reconstruction and hence the three-dimensional flow measurement by single camera-based LF-PIV. [ABSTRACT FROM AUTHOR]

Molecular dynamics simulation was applied to investigate the interaction between precipitate and 10 1 ¯ 2 twin in Mg-Al alloy in this work. Three sets of simulation including twin nucleation, propagation and growth affected by precipitate were performed. The results show the introduction of precipitate has little effect on neither the homogeneous twin nucleation at random site inside the grain nor the heterogeneous nucleation at grain boundary. During twin propagation, the blocking effect of precipitate to the twin tip depends on its size. The twin tip can bypass the precipitate when the precipitate is not long enough. When the length of precipitate is much larger than the thickness of twin, the twin tip will be completely blocked by the precipitate with elastic bending occurring at the junction. During the twin growth, it is found that the precipitate plays not only an obstacle but a source for the twinning dislocations gliding along the twin boundary. The blocking effect of precipitate on twin growth shows a size effect. The influences of width and thickness on precipitation hardening are significantly greater than that of length. [ABSTRACT FROM AUTHOR]

This paper proposes a light field (LF) three-dimensional (3D) particle image velocimetry (PIV) method based on a digital refocused algorithm and 3D U-Net neural network for 3D three-component (3D-3C) velocity measurement. A digital refocused algorithm is used to generate a stack of LF-refocused images of tracer particles for establishing the 3D U-Net. The 3D U-Net is then used for the 3D particle field reconstruction. Based on a pair of 3D particle fields, the 3D-3C velocity field is obtained through a 3D cross correlation algorithm. Numerical simulations and experiments are conducted to analyze the accuracy and efficiency of the proposed method. The simulation results show that the elongation along the depth direction and the efficiency of the 3D particle field reconstruction are improved by the 3D U-Net. The 3D U-Net also provides a better correlation coefficient. The experimental results show that the reconstruction time of the proposed method is ∼220 s which is 10 times faster than the LF tomographic PIV. This further demonstrates that the proposed method improves the reconstruction efficiency without affecting the accuracy of velocity measurement. [ABSTRACT FROM AUTHOR]