Time-of-Flight Camera Calibration for Improved 3D Reconstruction of Indoor Scenes

2014 ◽  
Author(s):  
Meng Xie ◽  
Jeremy R. Cooperstock
Keyword(s):  
3D Reconstruction ◽  
Camera Calibration ◽  
Time Of Flight ◽  
Indoor Scenes

Acquisition of Point Cloud Data in 3D Reconstruction

2014 ◽  
Vol 536-537 ◽  
pp. 213-217
Author(s):  
Meng Qiang Zhu ◽  
Jie Yang
Keyword(s):  
3D Reconstruction ◽  
Camera Calibration ◽  
Point Cloud ◽  
Corner Detection ◽  
Projection Mapping ◽  
Cloud Data ◽  
3D Space ◽  
Dense Point ◽  
Left And Right ◽  
Object Depth

This paper takes the following measures to solve the problem of 3D reconstruction. Camera calibration is based on chessboard, taking several different attitude images. Use corner point coordinates by corner detection to process camera calibration. The calibration result is important to be used to correct the distorted image. Next, the left and right images should be matched to find out the object surface points’ imaging position respectively so that the object depth can be calculated by triangulation. According to the inverse process of projection mapping, we can project the object depth and disparity information into 3D space. As a result, we can obtain dense point cloud, which is ready for 3D reconstruction.

Three-Dimensional Object Surface Reconstruction Based on Camera Calibration and SIFT

2015 ◽  
Vol 719-720 ◽  
pp. 1191-1197 ◽  
Author(s):  
Jun Zhang ◽  
Long Ye ◽  
Qin Zhang ◽  
Jing Ling Wang
Keyword(s):  
3D Reconstruction ◽  
Camera Calibration ◽  
Image Matching ◽  
Three Dimensional ◽  
Target Object ◽  
Object A ◽  
Binocular Stereo Vision ◽  
Binocular Stereo ◽  
Dimensional Object

This paper is focused on camera calibration, image matching, both of which are the key issues in three-dimensional (3D) reconstruction. In terms of camera calibration firstly, we adopt the method based on the method proposed by Zhengyou Zhang. In addition to this, it is selective for us to deal with tangential distortion. In respect of image matching, we use the SIFT algorithm, which is invariant to image translation, scaling, rotation, and partially invariant to illumination changes and to affine or 3D projections. It performs well in the follow-up matching the corresponding points. Lastly, we perform 3D reconstruction of the surface of the target object. A Graphical User Interface is designed to help us to realize the key function of binocular stereo vision, with better visualization. Apparently, the entire GUI brings convenience to the follow-up work.

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