scholarly journals Occluded-Object 3D Reconstruction Using Camera Array Synthetic Aperture Imaging

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 607 ◽  
Author(s):  
Zhao Pei ◽  
Yawen Li ◽  
Miao Ma ◽  
Jun Li ◽  
Chengcai Leng ◽  
...  
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Synthetic Aperture ◽  
Object Reconstruction ◽  
Synthetic Aperture Imaging ◽  
The Past ◽  
Camera Array ◽  
Occluded Objects ◽  
Great Progress ◽  
Novel Method

With the three-dimensional (3D) coordinates of objects captured by a sequence of images taken in different views, object reconstruction is a technique which aims to recover the shape and appearance information of objects. Although great progress in object reconstruction has been made over the past few years, object reconstruction in occlusion situations remains a challenging problem. In this paper, we propose a novel method to reconstruct occluded objects based on synthetic aperture imaging. Unlike most existing methods, which either assume that there is no occlusion in the scene or remove the occlusion from the reconstructed result, our method uses the characteristics of synthetic aperture imaging that can effectively reduce the influence of occlusion to reconstruct the scene with occlusion. The proposed method labels occlusion pixels according to variance and reconstructs the 3D point cloud based on synthetic aperture imaging. Accuracies of the point cloud are tested by calculating the spatial difference between occlusion and non-occlusion conditions. The experiment results show that the proposed method can handle the occluded situation well and demonstrates a promising performance.

Three-dimensional bubble field resolution using synthetic aperture imaging: application to a plunging jet

2012 ◽  
Vol 53 (3) ◽  
pp. 839-861 ◽  
Author(s):  
Jesse Belden ◽  
Sai Ravela ◽  
Tadd T. Truscott ◽  
Alexandra H. Techet
Keyword(s):  
Three Dimensional ◽  
Synthetic Aperture ◽  
Synthetic Aperture Imaging ◽  
Imaging Application

scholarly journals Refocusing Algorithm Based on Nonlinear SAI System

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Bin Liu ◽  
Yue Luo ◽  
Yi-Hua Pan ◽  
Wen-Min Yan ◽  
Xin-Yu Zhang
Keyword(s):  
Light Field ◽  
Linear Array ◽  
Optical Field ◽  
Synthetic Aperture ◽  
Camera Array ◽  
Occluded Objects ◽  
Field Information ◽  
Different Depths ◽  
Camera Arrays ◽  
Partially Occluded

Synthetic aperture imaging (SAI) technology gets the light field information of the scene through the camera array. With the large virtual aperture, it can effectively acquire the information of the partially occluded object in the scene, and then we can focus on the arbitrary target plane corresponding to the reference perspective through the refocus algorithm. Meanwhile, objects that deviate from the plane will be blurred to varying degrees. However, when the object to be reconstructed in the scene is occluded by the complex foreground, the optical field information of the target cannot be effectively detected due to the limitation of the linear array. In order to deal with these problems, this paper proposes a nonlinear SAI method. This method can obtain the occluded object’s light field information reliably by using the nonlinear array. Experiments are designed for the nonlinear SAI, and refocusing is performed for the occluded objects with different camera arrays, different depths, and different distribution intervals. The results demonstrate that the method proposed in this paper is advanced than the traditional SAI method based on linear array.

Research on Threedimensional Target Reconstruction Based on Spotlight Mode Inverse Synthetic Aperture Imaging Ladar Group

2013 ◽  
Vol 42 (8) ◽  
pp. 973-977
Author(s):  
刘智超 LIU Zhichao ◽  
杨进华 YANG Jinhua ◽  
王晨阳 WANG Chenyang ◽  
赵鑫 ZHAO Xin
Keyword(s):  
Three Dimensional ◽  
Synthetic Aperture ◽  
Synthetic Aperture Imaging

scholarly journals Facile fabrication of functional bra cup by an automated dispensing system

2019 ◽  
Vol 70 (05) ◽  
pp. 421-425
Author(s):  
YING ZHANG ◽  
TAO LI ◽  
FENG-YUAN ZOU ◽  
CHENG-HA YU ◽  
LEI DU
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Measuring Error ◽  
Point Cloud Data ◽  
3D Scanner ◽  
Cloud Data ◽  
Injection Process ◽  
Intimate Apparel ◽  
Facile Fabrication ◽  
Novel Method

The goal of the study is to develop a novel method to manufacture the functional bras. The high precision three-dimensional (3D) scanner was employed to get the point cloud data. A fixed mount was invented to keep the bra cup stable and decrease measuring error. A bottom holder was prepared by the 3D printer to place the bra cup during the injection process. Furthermore, the injection points coordinate values and the injection volumes can be determined based on the 3D image of the bra cup and the thickness of those positions. At last, the three-axis automatic robot which was coupled with a precision liquid dispenser is used to inject the microcapsules solution into the bra cup for the preparation of functional intimate apparel. The proposed method was verified to be feasible and effective through a practical example.

scholarly journals A Novel Method for Density Analysis of Repaired Point Cloud with Holes Based on Image Data

2021 ◽  
Vol 13 (17) ◽  
pp. 3417
Author(s):  
Yibo He ◽  
Zhenqi Hu ◽  
Kan Wu ◽  
Rui Wang
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Image Data ◽  
Image Point ◽  
Point Cloud Data ◽  
Three Dimensional Model ◽  
Cloud Data ◽  
Density Analysis ◽  
Novel Method ◽  
Density Image

Repairing point cloud holes has become an important problem in the research of 3D laser point cloud data, which ensures the integrity and improves the precision of point cloud data. However, for the point cloud data with non-characteristic holes, the boundary data of point cloud holes cannot be used for repairing. Therefore, this paper introduces photogrammetry technology and analyzes the density of the image point cloud data with the highest precision. The 3D laser point cloud data are first formed into hole data with sharp features. The image data are calculated into six density image point cloud data. Next, the barycenterization Bursa model is used to fine-register the two types of data and to delete the overlapping regions. Then, the cross-section is used to evaluate the precision of the combined point cloud data to get the optimal density. A three-dimensional model is constructed for this data and the original point cloud data, respectively and the surface area method and the deviation method are used to compare them. The experimental results show that the ratio of the areas is less than 0.5%, and the maximum standard deviation is 0.0036 m and the minimum is 0.0015 m.

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