Mutual information-based depth estimation and 3D reconstruction for image-based rendering systems

2012 ◽  
Author(s):  
Zhenyu Zhu
Keyword(s):  
Mutual Information ◽  
3D Reconstruction ◽  
Depth Estimation ◽  
Image Based Rendering

Hybrid 3D reconstruction and image-based rendering techniques for reality modeling

2000 ◽  
Author(s):  
Vitor Sequeira ◽  
Erik Wolfart ◽  
Emanuele Bovisio ◽  
Ester Biotti ◽  
Joao G. M. Goncalves
Keyword(s):  
3D Reconstruction ◽  
Image Based Rendering ◽  
Rendering Techniques

Face Depth Estimation and 3D Reconstruction

2020 ◽  
Author(s):  
Alba Terese Baby ◽  
Aleesha Andrews ◽  
Amal Dinesh ◽  
Amal Joseph ◽  
V.K Anjusree
Keyword(s):  
3D Reconstruction ◽  
Depth Estimation

scholarly journals Deteksi Tungkai Bayi Pada Image Sequence Berbasis Vector Depth Estimation

2021 ◽  
Vol 7 (3) ◽  
pp. 35-42
Author(s):  
Faisal Lutfi Afriansyah ◽  
Niyalatul Muna
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
3D Reconstruction ◽  
Reference Frame ◽  
Fundamental Matrix ◽  
Motion Vector ◽  
Depth Estimation ◽  
Image Sequence ◽  
Image Sequences ◽  
The Right

Image processing in the image sequence for pattern recognition can be a solution for detecting limb movements in infants after surgery, but the camera is not calibrated. So we need the right method solution to be able to detect these conditions. This happens to cameras that are generally not calibrated and do not have the feature to calculate the vector depth for 3D reconstruction. Because to detect and find limb movement depth is needed to be able to do 3D reconstruction, because it is not only based on the x and y parameters but also z so that with the additional parameters it makes it easier to analyze the motion of the motion axis and the motion vector. This paper discusses a method for detecting 2D motion into a 3D-based motion vector by sequencing the image sequence image then finding the point of transfer of the motion frame destination from the frame reference frame by obtaining the depth (depth vector) using the fundamental matrix from the generated motion vector. This method is recommended because it can perform 3D reconstruction from input in the form of 2D image sequences by calculating the intrinsic parameters so that 3D reconstruction can be carried out. So that the limb vector movement in infants that was originally 2D can be reconstructed into 3D based and makes it easier to carry out the analysis because of the additional parameters.

scholarly journals DEPTH ESTIMATION USING MULTIWAVELET ANALYSIS BASED STEREO VISION APPROACH

2008 ◽  
Vol 06 (03) ◽  
pp. 481-497 ◽  
Author(s):  
ASIM BHATTI ◽  
SAEID NAHAVANDI
Keyword(s):  
3D Reconstruction ◽  
Stereo Vision ◽  
Die Casting ◽  
Reconstruction Algorithm ◽  
Cost Effective ◽  
Depth Estimation ◽  
Input Image ◽  
Fluorescent Light ◽  
Foundry Industry ◽  
Die Castings

The problem of dimensional defects in aluminum die-casting is widespread throughout the foundry industry and their detection is of paramount importance in maintaining product quality. Due to the unpredictable factory environment and metallic, with highly reflective, nature of aluminum die-castings, it is extremely hard to estimate true dimensionality of the die-casting, autonomously. In this work, we propose a novel robust 3D reconstruction algorithm capable of reconstructing dimensionally accurate 3D depth models of the aluminum die-castings. The developed system is very simple and cost effective as it consists of only a stereo camera pair and a simple fluorescent light. The developed system is capable of estimating surface depths within the tolerance of 1.5 mm. Moreover, the system is invariant to illuminative variations and orientation of the objects in the input image space, which makes the developed system highly robust. Due to its hardware simplicity and robustness, it can be implemented in different factory environments without a significant change in the setup.

PANORAMIC 3D RECONSTRUCTION USING STEREO MULTI-PERSPECTIVE PANORAMA

2010 ◽  
Vol 24 (06) ◽  
pp. 867-896 ◽  
Author(s):  
WEI JIANG ◽  
SHIGEKI SUGIMOTO ◽  
MASATOSHI OKUTOMI
Keyword(s):  
3D Reconstruction ◽  
Depth Map ◽  
Depth Estimation ◽  
Vertical Axis ◽  
Large Field ◽  
Baseline Length ◽  
Novel Approach ◽  
Stereo Cameras ◽  
Spatio Temporal ◽  
Rotating Camera

In this paper, we present a novel approach to imaging a panoramic (360°) environment and computing its dense depth map. Our approach adopts a multi-baseline stereo strategy using a set of multi-perspective panoramas where large baseline lengths are available. We design two image acquisition rigs for capturing such multi-perspective panoramas. The first one is composed of two parallel stereo cameras. By rotating the rig about a vertical axis, we generate four multi-perspective panoramas by resampling the regular perspective images captured by the stereo cameras. Then a depth map is estimated from the four multi-perspective panoramas and an original perspective image using a multi-baseline matching technique with different types of epipolar constraints. The second one is composed of a single camera and two mirrors. By rotating the rig, we acquire a spatio-temporal volume that is made up of the sequential images captured by the camera. Then we estimate a depth map by extracting trajectories from the spatio-temporal volume by using a multi-baseline stereo technique by considering occlusions. We can consider both rotating rigs as a single rotating camera with a very large field of view (FOV), that offers a large baseline length in depth estimation. In addition, compared with a previous approach using two multi-perspective panoramas from a single rotating camera, our approach can reduce matching errors due to image noise, repeated patterns, and occlusions by multi-baseline stereo techniques. Experimental results using both synthetic and real images show that our approach produces high quality panoramic 3D reconstruction.

scholarly journals Three-Dimensional Reconstruction of Light Field Based on Phase Similarity

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7734
Author(s):  
Wei Feng ◽  
Junhui Gao ◽  
Tong Qu ◽  
Shiqi Zhou ◽  
Daxing Zhao
Keyword(s):  
3D Reconstruction ◽  
Light Field ◽  
Three Dimensional ◽  
Ground Truth ◽  
Depth Estimation ◽  
Estimation Algorithm ◽  
Morphological Processing ◽  
Disparity Estimation ◽  
Reconstruction Method ◽  
Disparity Map

Light field imaging plays an increasingly important role in the field of three-dimensional (3D) reconstruction because of its ability to quickly obtain four-dimensional information (angle and space) of the scene. In this paper, a 3D reconstruction method of light field based on phase similarity is proposed to increase the accuracy of depth estimation and the scope of applicability of epipolar plane image (EPI). The calibration method of the light field camera was used to obtain the relationship between disparity and depth, and the projector calibration was removed to make the experimental procedure more flexible. Then, the disparity estimation algorithm based on phase similarity was designed to effectively improve the reliability and accuracy of disparity calculation, in which the phase information was used instead of the structure tensor, and the morphological processing method was used to denoise and optimize the disparity map. Finally, 3D reconstruction of the light field was realized by combining disparity information with the calibrated relationship. The experimental results showed that the reconstruction standard deviation of the two objects was 0.3179 mm and 0.3865 mm compared with the ground truth of the measured objects, respectively. Compared with the traditional EPI method, our method can not only make EPI perform well in a single scene or blurred texture situations but also maintain good reconstruction accuracy.

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