scholarly journals Stereo Matching and 3D Reconstruction via an Omnidirectional Stereo Sensor

10.5772/6016 ◽  
2008 ◽  
Author(s):  
Lei He ◽  
Chuanjiang Luo ◽  
Feng Zhu ◽  
Yingming Hao
Keyword(s):  
3D Reconstruction ◽  
Stereo Matching

scholarly journals Vision System of Mobile Robot Combining Binocular and Depth Cameras

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Yuxiang Yang ◽  
Xiang Meng ◽  
Mingyu Gao
Keyword(s):  
Mobile Robot ◽  
3D Reconstruction ◽  
Trajectory Planning ◽  
Stereo Matching ◽  
Vision System ◽  
Depth Camera ◽  
Structural Constraints ◽  
Depth Cameras ◽  
Stereo Cameras ◽  
Binocular Stereo

In order to optimize the three-dimensional (3D) reconstruction and obtain more precise actual distances of the object, a 3D reconstruction system combining binocular and depth cameras is proposed in this paper. The whole system consists of two identical color cameras, a TOF depth camera, an image processing host, a mobile robot control host, and a mobile robot. Because of structural constraints, the resolution of TOF depth camera is very low, which difficultly meets the requirement of trajectory planning. The resolution of binocular stereo cameras can be very high, but the effect of stereo matching is not ideal for low-texture scenes. Hence binocular stereo cameras also difficultly meet the requirements of high accuracy. In this paper, the proposed system integrates depth camera and stereo matching to improve the precision of the 3D reconstruction. Moreover, a double threads processing method is applied to improve the efficiency of the system. The experimental results show that the system can effectively improve the accuracy of 3D reconstruction, identify the distance from the camera accurately, and achieve the strategy of trajectory planning.

A fast stereo matching algorithm for 3D reconstruction of internal organs in laparoscopic surgery

2008 ◽  
Author(s):  
Yoshimichi Okada ◽  
Takeshi Koishi ◽  
Suguru Ushiki ◽  
Toshiya Nakaguchi ◽  
Norimichi Tsumura ◽  
...  
Keyword(s):  
Laparoscopic Surgery ◽  
3D Reconstruction ◽  
Stereo Matching ◽  
Internal Organs ◽  
Matching Algorithm

Image Stereo Correspondence Method for Stereo Vision

2013 ◽  
Vol 475-476 ◽  
pp. 337-341
Author(s):  
Ai Hua Chen ◽  
Cheng Hui Gao ◽  
Bing Wei He
Keyword(s):  
3D Reconstruction ◽  
Stereo Vision ◽  
Stereo Matching ◽  
Target Recognition ◽  
Future Research ◽  
Stereo Correspondence ◽  
Research Issues ◽  
Vision Measurement ◽  
The Core ◽  
Core Technology

Image stereo correspondence is the core technology of stereo vision. It has been widely studied and applied in the fields such as 3D reconstruction, vision measurement and target recognition. According to characteristics and application of stereo matching technology, the image stereo correspondence methods can be classified into three categories: local stereo correspondence, global stereo correspondence and semi-global stereo correspondence. Some image stereo correspondence solutions and problems are emphatically analyzed. Finally some future research issues on image stereo correspondence are highlighted.

scholarly journals 3D reconstruction of curvilinear structures with stereo matching deep convolutional neural networks

2022 ◽  
pp. 113460
Author(s):  
Okan Altingövde ◽  
Anastasiia Mishchuk ◽  
Gulnaz Ganeeva ◽  
Emad Oveisi ◽  
Cecile Hebert ◽  
...  
Keyword(s):  
Neural Networks ◽  
3D Reconstruction ◽  
Convolutional Neural Networks ◽  
Stereo Matching ◽  
Deep Convolutional Neural Networks ◽  
Curvilinear Structures

A New Method on Super Pixel Reducing Stereo Matching Time of Integrated Imaging

2020 ◽  
pp. 2154014
Author(s):  
Xue-Guang Wang ◽  
Ming Li ◽  
Lei Zhang ◽  
Hui Zhao ◽  
Thelma D. Palaoag
Keyword(s):  
3D Reconstruction ◽  
Conventional Method ◽  
Stereo Vision ◽  
Stereo Matching ◽  
Technical Difficulty ◽  
New Method ◽  
Matching Algorithm ◽  
The Core ◽  
Pixel Matching ◽  
Novel Method

Stereo vision and 3D reconstruction technologies are increasingly concerned in many fields. Stereo matching algorithm is the core of stereo vision and also a technical difficulty. A novel method based on super pixels is mentioned in this paper to reduce the calculating amount and the time. Stereo images from University of Tsukuba are used to test our method. The proposed method spends only 1% of the time spent by the conventional method. Through a two-step super-pixel matching optimization, it takes 6.72 s to match a picture, which is 12.96% of the pre-optimization.

scholarly journals A New Extension of the Rank Transform for Stereo Matching

2011 ◽  
Vol 2-3 ◽  
pp. 182-187 ◽  
Author(s):  
Ge Zhao ◽  
Ying Kui Du ◽  
Yan Dong Tang
Keyword(s):  
3D Reconstruction ◽  
Transition Zone ◽  
Stereo Matching ◽  
Color Images ◽  
Smooth Transition ◽  
Discriminative Power ◽  
Rank Transform ◽  
Left And Right ◽  
Disparity Maps ◽  
Image Pixels

Stereo matching methods often use rank transform to deal with image distortions and brightness differences prior to matching but a pixel in the rank transformed image may look more similar to its neighbor, which would cause matching ambiguity. We tackle this problem with two proposals. Firstly, instead of using two values 0 and 1,we increase the discriminative power of the rank transform by using a linear, smooth transition zone between 0 and 1 for intensities that are close together. Secondly, we propose a new Bayesian stereo matching model by not only considering the similarity between left and right image pixels but also considering the ambiguity level of them in their own image independently. We test our algorithm on both intensity and color images with brightnesss differences. Corresponding 2D disparity maps and 3D reconstruction results verify the effectiveness of our method.

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