Three-Dimensional Reconstruction of Dilute Bubbly Flow Field with Light-Field Images Based on Deep Learning Method

2021 ◽  
pp. 1-1
Author(s):  
Hongyi Wang ◽  
Hongyu Wang ◽  
Xinjun Zhu ◽  
Limei Song ◽  
Qinghua Guo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Flow Field ◽  
Light Field ◽  
Bubbly Flow ◽  
Three Dimensional ◽  
Learning Method ◽  
Three Dimensional Reconstruction ◽  
Dimensional Reconstruction

Three-Dimensional Reconstruction of Micro-Scale Flow Field Based on Light Field Microscopic Imaging

2019 ◽  
Vol 39 (10) ◽  
pp. 1011002
Author(s):  
宋祥磊 Song Xianglei ◽  
李舒 Li Shu ◽  
顾梦涛 Gu Mengtao ◽  
张彪 Zhang Biao ◽  
许传龙 Xu Chuanlong
Keyword(s):  
Flow Field ◽  
Light Field ◽  
Three Dimensional ◽  
Three Dimensional Reconstruction ◽  
Microscopic Imaging ◽  
Micro Scale ◽  
Dimensional Reconstruction

Three dimensional reconstruction using a lenslet light field camera

2018 ◽  
Author(s):  
Wentao Zhang ◽  
Shengqian Chang ◽  
Xiao Tao ◽  
Chang Wang ◽  
Chenning Tao ◽  
...  
Keyword(s):  
Light Field ◽  
Three Dimensional ◽  
Three Dimensional Reconstruction ◽  
Dimensional Reconstruction

scholarly journals Calibration and Auto-Refinement for Light Field Cameras

2021 ◽  
Author(s):  
Yuriy Anisimov ◽  
Gerd Reis ◽  
Didier Stricker
Keyword(s):  
Nonlinear Optimization ◽  
Camera Calibration ◽  
Light Field ◽  
Three Dimensional ◽  
Synthetic Data ◽  
Light Fields ◽  
Three Dimensional Reconstruction ◽  
Parameters Extraction ◽  
Dimensional Reconstruction ◽  
Camera Parameters

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.

Three-dimensional reconstruction of super-resolved white-light interferograms based on deep learning

2021 ◽  
Vol 145 ◽  
pp. 106663
Author(s):  
Lei Xin ◽  
Xin Liu ◽  
Zhongming Yang ◽  
Xingyu Zhang ◽  
Zhishan Gao ◽  
...  
Keyword(s):  
Deep Learning ◽  
White Light ◽  
Three Dimensional ◽  
Three Dimensional Reconstruction ◽  
Dimensional Reconstruction

Methods for three-dimensional reconstruction of cellular components within a thick section

1986 ◽  
Vol 44 ◽  
pp. 18-21
Author(s):  
J. Frank ◽  
B. F. McEwen ◽  
M. Radermacher ◽  
C. L. Rieder
Keyword(s):  
Tilt Angle ◽  
Tomographic Reconstruction ◽  
3D Structure ◽  
Three Dimensional ◽  
Thick Section ◽  
Three Dimensional Reconstruction ◽  
Axis Ratio ◽  
Dimensional Reconstruction ◽  
Cellular Components

The tomographic reconstruction from multiple projections of cellular components, within a thick section, offers a way of visualizing and quantifying their three-dimensional (3D) structure. However, asymmetric objects require as many views from the widest tilt range as possible; otherwise the reconstruction may be uninterpretable. Even if not for geometric obstructions, the increasing pathway of electrons, as the tilt angle is increased, poses the ultimate upper limitation to the projection range. With the maximum tilt angle being fixed, the only way to improve the faithfulness of the reconstruction is by changing the mode of the tilting from single-axis to conical; a point within the object projected with a tilt angle of 60° and a full 360° azimuthal range is then reconstructed as a slightly elliptic (axis ratio 1.2 : 1) sphere.

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