Three-step Alignment Approach for Fitting a Normalized Mask of a Person Rotating in A-Pose or T-Pose Essential for 3D Reconstruction based on 2D Images and CGI Derived Reference Target Pose

The present work provides a new approach for 3D image reconstruction of gas-liquid two-phase flow (GLF) in mini-channels based on a new optical sensor. The sensor consists of a vertical and a horizontal photodiode array. Firstly, with the optical signals obtained by the vertical array, a measurement model developed by Support Vector Regression (SVR) was used to determine the cross-sectional information. The determined information was further used to reconstruct cross-sectional 2D images. Then, the gas velocity was calculated according to the signals obtained by the horizontal array, and the spatial interval of the 2D images was determined. Finally, 3D images were reconstructed by piling up the 2D images. In this work, the cross-sectional gas-liquid interface was considered as circular, and high-speed visualization was utilized to provide the reference values. The image deformation caused by channel wall was also considered. Experiments of slug flow in a channel with an inner diameter of 4.0 mm were carried out. The results verify the feasibility of the proposed 3D reconstruction method. The proposed method has the advantages of simple construct, low cost, and easily multipliable. The reconstructed 3D images can provide detailed and undistorted information of flow structure, which could further improve the measurement accuracy of other important parameters of gas-liquid two-phase flow, such as void fraction, pressure drop, and flow pattern.

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From 2D Images to 3D Tangible Models: Autostereoscopic and Haptic Visualization of Martian Rocks in Virtual Environments

Presence Teleoperators & Virtual Environments ◽

10.1162/pres.16.1.1 ◽

2007 ◽

Vol 16 (1) ◽

pp. 1-15 ◽

Cited By ~ 3

Author(s):

Cagatay Basdogan

Keyword(s):

3D Reconstruction ◽

3D Models ◽

Rock Surface ◽

Surface Model ◽

Stereo Images ◽

Martian Surface ◽

Reconstruction Process ◽

Low Transmission ◽

2D Images ◽

Multimodal Visualization

A planetary rover acquires a large collection of images while exploring its surrounding environment. For example, 2D stereo images of the Martian surface captured by the lander and the Sojourner rover during the Mars Pathfinder mission in 1997 were transmitted to Earth for scientific analysis and navigation planning. Due to the limited memory and computational power of the Sojourner rover, most of the images were captured by the lander and then transmitted to Earth directly for processing. If these images were merged together at the rover site to reconstruct a 3D representation of the rover's environment using its on-board resources, more information could potentially be transmitted to Earth in a compact manner. However, construction of a 3D model from multiple views is a highly challenging task to accomplish even for the new generation rovers (Spirit and Opportunity) running on the Mars surface at the time this article was written. Moreover, low transmission rates and communication intervals between Earth and Mars make the transmission of any data more difficult. We propose a robust and computationally efficient method for progressive transmission of multi-resolution 3D models of Martian rocks and soil reconstructed from a series of stereo images. For visualization of these models on Earth, we have developed a new multimodal visualization setup that integrates vision and touch. Our scheme for 3D reconstruction of Martian rocks from 2D images for visualization on Earth involves four main steps: a) acquisition of scans: depth maps are generated from stereo images, b) integration of scans: the scans are correctly positioned and oriented with respect to each other and fused to construct a 3D volumetric representation of the rocks using an octree, c) transmission: the volumetric data is encoded and progressively transmitted to Earth, d) visualization: a surface model is reconstructed from the transmitted data on Earth and displayed to a user through a new autostereoscopic visualization table and a haptic device for providing touch feedback. To test the practical utility of our approach, we first captured a sequence of stereo images of a rock surface from various viewpoints in JPL MarsYard using a mobile cart and then performed a series of 3D reconstruction experiments. In this paper, we discuss the steps of our reconstruction process, our multimodal visualization system, and the tradeoffs that have to be made to transmit multiresolution 3D models to Earth in an efficient manner under the constraints of limited computational resources, low transmission rate, and communication interval between Earth and Mars.

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Towards Rapid 3D Reconstruction using Conventional X-Ray for Intraoperative Orthopaedic Applications

Applied Signal and Image Processing ◽

10.4018/978-1-60960-477-6.ch018 ◽

2011 ◽

pp. 309-323 ◽

Cited By ~ 1

Author(s):

Simant Prakoonwit

Keyword(s):

3D Reconstruction ◽

Statistical Shape Model ◽

Shape Model ◽

X Ray ◽

Physical Constraints ◽

Surgical Tools ◽

Reconstructed Surfaces ◽

Statistical Shape ◽

Post Operation ◽

2D Images

A rapid 3D reconstruction of bones and other structures during an operation is an important issue. However, most of existing technologies are not feasible to be implemented in an intraoperative environment. Normally, a 3D reconstruction has to be done by a CT or an MRI pre operation or post operation. Due to some physical constraints, it is not feasible to utilise such machine intraoperatively. A special type of MRI has been developed to overcome the problem. However, all normal surgical tools and instruments cannot be employed. This chapter discusses a possible method to use a small number, e.g. 5, of conventional 2D X-ray images to reconstruct 3D bone and other structures intraoperatively. A statistical shape model is used to fit a set of optimal landmarks vertices, which are automatically created from the 2D images, to reconstruct a full surface. The reconstructed surfaces can then be visualised and manipulated by surgeons or used by surgical robotic systems.

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3D Reconstruction of facial structures from 2D images for cosmetic surgery

2011 International Conference on Recent Trends in Information Technology (ICRTIT) ◽

10.1109/icrtit.2011.5972446 ◽

2011 ◽

Author(s):

T. Senthil Kumar ◽

P. B. Rakesh

Keyword(s):

3D Reconstruction ◽

Cosmetic Surgery ◽

2D Images

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3D Imaging of Bistatic Inverse Synthetic Aperture Radar Based on the Factorization Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.347-350.1091 ◽

2013 ◽

Vol 347-350 ◽

pp. 1091-1095

Author(s):

Xin Wang ◽

Chao Xuan Shang

Keyword(s):

3D Reconstruction ◽

Synthetic Aperture Radar ◽

3D Imaging ◽

Factorization Method ◽

Synthetic Aperture ◽

Inverse Synthetic Aperture Radar ◽

Scattering Centers ◽

Reconstruction Model ◽

2D Images ◽

Aperture Radar

Based on the geometrical projection of 3D scattering centers on the line of radar sight, a new method of bistatic inverse synthetic aperture radar 3D Imaging is proposed. In the method, Range-Doppler algorithm gives a sequence of 2D images of target during its motion, and 3D reconstruction of target geometry is completed by the factorization method. We analyzed the theory of Bi-ISAR 3D imaging, deduced the process of the factorization method, and introduced the hierarchical reconstruction model. The simulation verified the validity of the paper.

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Robust Denoising of Cryo-EM Images via β-GAN

10.21203/rs.3.rs-139567/v1 ◽

2021 ◽

Author(s):

Hanlin Gu ◽

Ilona Unarta ◽

Xuhui Huang ◽

Yuan Yao

Keyword(s):

3D Reconstruction ◽

3D Structure ◽

Similarity Index ◽

Structural Similarity ◽

Generative Adversarial Networks ◽

Denoising Autoencoder ◽

80S Ribosome ◽

Noise Ratio ◽

2D Images ◽

Traditional Image

Abstract The cryo-electron microscopy (Cryo-EM) becomes popular for macromolecular structure determination. However, the 2D images which Cryo-EM detects are of high noise and often mixed with multiple heterogeneous conformations and contamination, imposing a challenge for denoising. Traditional image denoising methods and simple Denoising Autoencoder can not remove Cryo-EM image noise well when the signal-noise-ratio (SNR) of images is meager and contamination distribution is complex. Thus it is desired to develop new effective denoising techniques to facilitate further research such as 3D reconstruction, 2D conformation classification, and so on. In this paper, we approach the robust denoising problem for Cryo-EM images by introducing a family of Generative Adversarial Networks (GAN), called β-GAN, which is able to achieve robust estimate of certain distributional parameters under Huber contamination model with statistical optimality. To address the challenge of robust denoising where the traditional image generative model might be contaminated by a small portion of unknown outliers, β-GANs are exploited to enhance the robustness of denoising Autoencoder. The method is evaluated by both a simulated dataset on the Thermus aquaticus RNA Polymerase (RNAP) and a real dataset on the Plasmodium falciparum 80S ribosome dataset (EMPIRE-10028), in terms of Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and 3D Reconstruction as well. The results show that equipped with some designs of β-GANs and the robust ℓ1-Autoencoder, one can stabilize the training of GANs and achieve the state-of-the-art performance of robust denoising with low SNR data and against possible information contamination. Our proposed methodology thus provides an effective tool for robust denoising of Cryo-EM 2D images, which is helpful for 3D structure reconstruction.

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