A 3D Reconstruction Method Based on Images Dense Stereo Matching

2009 ◽  
Author(s):  
Ze-tao Jiang ◽  
Bi-na Zheng ◽  
Min Wu ◽  
Zhong-xiang Chen
Keyword(s):  
3D Reconstruction ◽  
Stereo Matching ◽  
Reconstruction Method ◽  
Dense Stereo Matching

scholarly journals Enhanced Soft 3D Reconstruction Method with an Iterative Matching Cost Update Using Object Surface Consensus

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6680
Author(s):  
Min-Jae Lee ◽  
Gi-Mun Um ◽  
Joungil Yun ◽  
Won-Sik Cheong ◽  
Soon-Yong Park
Keyword(s):  
3D Reconstruction ◽  
Stereo Matching ◽  
View Synthesis ◽  
Depth Image ◽  
Reconstruction Method ◽  
Object Surface ◽  
Plane Sweep ◽  
Depth Images ◽  
Research Areas ◽  
Matching Cost

In this paper, we propose a multi-view stereo matching method, EnSoft3D (Enhanced Soft 3D Reconstruction) to obtain dense and high-quality depth images. Multi-view stereo is one of the high-interest research areas and has wide applications. Motivated by the Soft3D reconstruction method, we introduce a new multi-view stereo matching scheme. The original Soft3D method is introduced for novel view synthesis, while occlusion-aware depth is also reconstructed by integrating the matching costs of the Plane Sweep Stereo (PSS) and soft visibility volumes. However, the Soft3D method has an inherent limitation because the erroneous PSS matching costs are not updated. To overcome this limitation, the proposed scheme introduces an update process of the PSS matching costs. From the object surface consensus volume, an inverse consensus kernel is derived, and the PSS matching costs are iteratively updated using the kernel. The proposed EnSoft3D method reconstructs a highly accurate 3D depth image because both the multi-view matching cost and soft visibility are updated simultaneously. The performance of the proposed method is evaluated by using structured and unstructured benchmark datasets. Disparity error is measured to verify 3D reconstruction accuracy, and both PSNR and SSIM are measured to verify the simultaneous enhancement of view synthesis.

scholarly journals AN AUTOMATIC 3D RECONSTRUCTION METHOD BASED ON MULTI-VIEW STEREO VISION FOR THE MOGAO GROTTOES

2015 ◽  
Vol XL-4/W5 ◽  
pp. 171-176 ◽  
Author(s):  
J. Xiong ◽  
S. Zhong ◽  
L. Zheng
Keyword(s):  
3D Reconstruction ◽  
Stereo Vision ◽  
Measurement System ◽  
Stereo Matching ◽  
Three Dimensional ◽  
Texture Mapping ◽  
Reconstruction Method ◽  
Epipolar Constraint ◽  
Mogao Grottoes ◽  
Binocular Stereo

This paper presents an automatic three-dimensional reconstruction method based on multi-view stereo vision for the Mogao Grottoes. 3D digitization technique has been used in cultural heritage conservation and replication over the past decade, especially the methods based on binocular stereo vision. However, mismatched points are inevitable in traditional binocular stereo matching due to repeatable or similar features of binocular images. In order to reduce the probability of mismatching greatly and improve the measure precision, a portable four-camera photographic measurement system is used for 3D modelling of a scene. Four cameras of the measurement system form six binocular systems with baselines of different lengths to add extra matching constraints and offer multiple measurements. Matching error based on epipolar constraint is introduced to remove the mismatched points. Finally, an accurate point cloud can be generated by multi-images matching and sub-pixel interpolation. Delaunay triangulation and texture mapping are performed to obtain the 3D model of a scene. The method has been tested on 3D reconstruction several scenes of the Mogao Grottoes and good results verify the effectiveness of the method.

3D Reconstruction Based on Hexagonal Pixel’s Dense Stereo Matching

2010 ◽  
Vol 20-23 ◽  
pp. 487-492 ◽  
Author(s):  
Ze Tao Jiang ◽  
Qing Hui Xiao ◽  
Ling Hong Zhu
Keyword(s):  
3D Reconstruction ◽  
Stereo Matching ◽  
Dynamic Range ◽  
Three Dimensional ◽  
Texture Mapping ◽  
Image Sequences ◽  
Feature Points ◽  
Feature Point Extraction ◽  
Dense Stereo Matching ◽  
New Feature

A new feature points extraction method is presented, which consider pixel as hexagonal. The method quasi increases the density of image pixel, expands the dynamic range of feature point extraction, increases the number of the features and resolves the problem of deformation of reconstruction which was leaded by lack of feature points. Firstly, the method was successful applied to sift operator of features extraction in this paper and then use dense stereo matching method to find the matching point of the image sequences. Secondly, through the RANSAC method to eliminate mistake matches, and by the camera matrix, calculate the corresponding points’ three-dimensional coordinates of space. Finally, the 3D model can be established through the partition merging triangulation method and texture mapping. Experimental results show that this method can get more accurate matches pairs and achieve a satisfactory effect of 3D reconstruction.

Dense stereo matching by region growing based on disparity space sampling

2009 ◽  
Vol 29 (10) ◽  
pp. 2690-2692
Author(s):  
Bao-hai YANG ◽  
Xiao-li LIU ◽  
Dai-feng ZHA
Keyword(s):  
Stereo Matching ◽  
Region Growing ◽  
Dense Stereo Matching ◽  
Disparity Space

3D RECONSTRUCTION OF BREAST CANCER FROM MAMMOGRAMS USING VOLUME RENDERING TECHNIQUES

2015 ◽  
Vol 75 (2) ◽  
Author(s):  
Ho Wei Yong ◽  
Abdullah Bade ◽  
Rajesh Kumar Muniandy
Keyword(s):  
Breast Cancer ◽  
3D Reconstruction ◽  
Computational Cost ◽  
Reconstruction Method ◽  
Cancer Model ◽  
Volume Reconstruction ◽  
The Past ◽  
Breast Cancer Model ◽  
Reconstruction Software ◽  
To Come

Over the past thirty years, a number of researchers have investigated on 3D organ reconstruction from medical images and there are a few 3D reconstruction software available on the market. However, not many researcheshave focused on3D reconstruction of breast cancer’s tumours. Due to the method complexity, most 3D breast cancer’s tumours reconstruction were done based on MRI slices dataeven though mammogram is the current clinical practice for breast cancer screening. Therefore, this research will investigate the process of creating a method that will be able to reconstruct 3D breast cancer’s tumours from mammograms effectively.  Several steps were proposed for this research which includes data acquisition, volume reconstruction, andvolume rendering. The expected output from this research is the 3D breast cancer’s tumours model that is generated from correctly registered mammograms. The main purpose of this research is to come up with a 3D reconstruction method that can produce good breast cancer model from mammograms while using minimal computational cost.

scholarly journals Fast 3D reconstruction method for differential phase contrast X-ray CT

2016 ◽  
Vol 24 (13) ◽  
pp. 14564 ◽  
Author(s):  
Michael T. McCann ◽  
Masih Nilchian ◽  
Marco Stampanoni ◽  
Michael Unser
Keyword(s):  
3D Reconstruction ◽  
Phase Contrast ◽  
Reconstruction Method ◽  
X Ray ◽  
Differential Phase ◽  
Differential Phase Contrast
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