Journal articles: 'Reconstruction 2D'

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Van Houten, Elijah E. W., Sajad Ghazavi, Guillaume Fle, Hari s. Nair, Boris Chayer, Ruchi Goswami, Salvatore Girardo, Jochen Guck, and Guy Cloutier. "2D boundary-condition-free nonlinear inversion technique applied to optical shear vibration induced microelastography." Journal of the Acoustical Society of America 155, no. 3_Supplement (March 1, 2024): A246. http://dx.doi.org/10.1121/10.0027380.

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Optical microelastography (OME) has emerged as a new technique for quantifying cellular mechanical properties. However, accurately reconstructing viscoelastic properties at the microscale level from noisy 2D displacement fields remains a challenge. This study introduces a 2D boundary-condition-free nonlinear inversion (2D-NoBC-NLI) approach, addressing challenges of interpreting noisy data and deducing full-field 3D displacements from 2D measurements. OME requires vibrating the cell and mapping the shear modulus based on wave-induced displacements within the cell. The shear modulus distribution is recovered via a coupled adjoint field NLI reconstruction to allow 2D-NoBC-NLI. Validation was conducted through numerical simulations at 36 kHz on a homogeneous sphere of 75 μm diameter and an assigned viscoelastic modulus, G*, of 800 + i150 Pa. The same reconstruction approach was also applied to experimental data obtained from polyacrylamide (PAAm) microbeads of the same diameter. Results demonstrated relative differences from true simulated values of 0.7% and 45% for storage and loss moduli, respectively, with a coefficient of variation under 1% for homogeneous regions. When applying this method to PAAm microbeads, viscoelastic reconstructions showed the potential of OME under experimental conditions. These findings highlight the accuracy of 2D-No BC-NLI reconstruction in OME for precise microscale characterization and mapping of the viscoelastic cell structure.

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Wen, Mingyun, and Kyungeun Cho. "Object-Aware 3D Scene Reconstruction from Single 2D Images of Indoor Scenes." Mathematics 11, no. 2 (January 12, 2023): 403. http://dx.doi.org/10.3390/math11020403.

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Recent studies have shown that deep learning achieves excellent performance in reconstructing 3D scenes from multiview images or videos. However, these reconstructions do not provide the identities of objects, and object identification is necessary for a scene to be functional in virtual reality or interactive applications. The objects in a scene reconstructed as one mesh are treated as a single object, rather than individual entities that can be interacted with or manipulated. Reconstructing an object-aware 3D scene from a single 2D image is challenging because the image conversion process from a 3D scene to a 2D image is irreversible, and the projection from 3D to 2D reduces a dimension. To alleviate the effects of dimension reduction, we proposed a module to generate depth features that can aid the 3D pose estimation of objects. Additionally, we developed a novel approach to mesh reconstruction that combines two decoders that estimate 3D shapes with different shape representations. By leveraging the principles of multitask learning, our approach demonstrated superior performance in generating complete meshes compared to methods relying solely on implicit representation-based mesh reconstruction networks (e.g., local deep implicit functions), as well as producing more accurate shapes compared to previous approaches for mesh reconstruction from single images (e.g., topology modification networks). The proposed method was evaluated on real-world datasets. The results showed that it could effectively improve the object-aware 3D scene reconstruction performance over existing methods.

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Caruana, Matthew, and Joseph G. Vella. "3D Facial Reconstruction from 2D Portrait Imagery." Information & Security: An International Journal 47, no. 3 (2020): 328–40. http://dx.doi.org/10.11610/isij.4724.

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Zhu, Linqi, Chong Zhang, Chaomo Zhang, Xueqing Zhou, Zhansong Zhang, Xin Nie, Weinan Liu, and Boyuan Zhu. "Challenges and Prospects of Digital Core-Reconstruction Research." Geofluids 2019 (May 20, 2019): 1–29. http://dx.doi.org/10.1155/2019/7814180.

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The simulation of various rock properties based on three-dimensional digital cores plays an increasingly important role in oil and gas exploration and development. The accuracy of 3D digital core reconstruction is important for determining rock properties. In this paper, existing 3D digital core-reconstruction methods are divided into two categories: 3D digital cores based on physical experiments and 3D digital core stochastic reconstructions based on two-dimensional (2D) slices. Additionally, 2D slice-based digital core stochastic reconstruction techniques are classified into four types: a stochastic reconstruction method based on 2D slice mathematical-feature statistical constraints, a stochastic reconstruction method based on statistical constraints that are related to 2D slice morphological characteristics, a physics process-based stochastic reconstruction method, and a hybrid stochastic reconstruction method. The progress related to these various stochastic reconstruction methods, the characteristics of constructed 3D digital cores, and the potential of these methods are analysed and discussed in detail. Finally, reasonable prospects are presented based on the current state of this research area. Currently, studies on digital core reconstruction, especially for the 3D digital core stochastic reconstruction method based on 2D slices, are still very rough, and much room for improvement remains. In particular, we emphasize the importance of evaluating functions, multiscale 3D digital cores, multicomponent 3D digital cores, and disciplinary intersection methods in the 3D construction of digital cores. These four directions should provide focus, alongside challenges, for this research area in the future. This review provides important insights into 3D digital core reconstruction.

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Marin, D., S. Ohrhallinger, and M. Wimmer. "SIGDT: 2D Curve Reconstruction." Computer Graphics Forum 41, no. 7 (October 2022): 25–36. http://dx.doi.org/10.1111/cgf.14654.

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Cardoen, Thorsten, Sam Leroux, and Pieter Simoens. "Iterative Online 3D Reconstruction from RGB Images." Sensors 22, no. 24 (December 13, 2022): 9782. http://dx.doi.org/10.3390/s22249782.

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3D reconstruction is the computer vision task of reconstructing the 3D shape of an object from multiple 2D images. Most existing algorithms for this task are designed for offline settings, producing a single reconstruction from a batch of images taken from diverse viewpoints. Alongside reconstruction accuracy, additional considerations arise when 3D reconstructions are used in real-time processing pipelines for applications such as robot navigation or manipulation. In these cases, an accurate 3D reconstruction is already required while the data gathering is still in progress. In this paper, we demonstrate how existing batch-based reconstruction algorithms lead to suboptimal reconstruction quality when used for online, iterative 3D reconstruction and propose appropriate modifications to the existing Pix2Vox++ architecture. When additional viewpoints become available at a high rate, e.g., from a camera mounted on a drone, selecting the most informative viewpoints is important in order to mitigate long term memory loss and to reduce the computational footprint. We present qualitative and quantitative results on the optimal selection of viewpoints and show that state-of-the-art reconstruction quality is already obtained with elementary selection algorithms.

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Cygan, Szymon, and Adriana Specyalska. "Comparison of three methods for reconstructing 3D motion from 2D video recordings for low cost gait analysis systems." Polish Journal of Medical Physics and Engineering 27, no. 4 (December 1, 2021): 271–77. http://dx.doi.org/10.2478/pjmpe-2021-0032.

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Abstract Purpose: The aim of this study was to quantify the accuracy of 3D trajectory reconstructions performed from two planar video recordings, using three different reconstruction methods. Additionally, the recordings were carried out using easily available equipment, like built-in cellphone cameras, making the methods suitable for low-cost applications. Methods: A setup for 3D motion tracking was constructed and used to acquire 2D video recordings subsequently used to reconstruct the 3D trajectories by 1) merging appropriate coordinates, 2) merging coordinates with proportional scaling, and 3) calculating the 3D position based on markers’ projections on the viewing plane. As experimental verification, two markers moving at a fixed distance of 98.9 cm were used to assess the consistency of results. Next, gait analysis in five volunteers was carried out to quantify the differences resulting from different reconstruction methods. Results: Quantitative evaluation of the investigated 3D trajectories reconstruction methods showed significant differences between those methods, with the worst reconstruction approach resulting in a maximum error of 50% (standard deviation 13%), while the best resulting in a maximum error of 1% (standard deviation 0.44%). The gait analysis results showed differences in mean angles obtained with each reconstruction method reaching only 2°, which can be attributed to the limited measurement volume. Conclusions: Reconstructing 3D trajectory from 2D views without accounting for the “perspective error” results in significant reconstruction errors. The third method described in this study enables a significant reduction of this issue. Combined with the proposed setup, it provides a functional, low-cost gait analysis system.

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Jiye, Ximen, and Shao Zhifeng. "Three-Dimensional Algebraic Reconstruction From Three Mutually Orthogonal Projections." Proceedings, annual meeting, Electron Microscopy Society of America 43 (August 1985): 306–7. http://dx.doi.org/10.1017/s0424820100118400.

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The classic reconstruction problem is that of reconstructing a 3D object from its 2D projections /1-5/. It is also well known that the principal difficulty in solving this problem in electron microscopy is that a very large number of independent projections are normally required. Recently it has been shown /3,4/ that if we restrict our attention to binary or Boolean objects, far fewer projections are needed in order to obtain an approximate solution. 2-dimensional solutions of ID projections were demonstrated using only four views and 3-dimensional reconstruction of 2D projections were obtained by dividing the projections into identifiable slices.In the present paper, an algebraic reconstruction technique (ART) has been studied which uses three mutually orthogonal projections.

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Erlandsson, K., and S. E. Strand. "3D reconstruction for 2D PET." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 391, no. 2 (June 1997): 369–74. http://dx.doi.org/10.1016/s0168-9002(97)00321-5.

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Hanhela, Matti, Antti Paajanen, Mikko J. Nissi, and Ville Kolehmainen. "Embedded Quantitative MRI T1ρ Mapping Using Non-Linear Primal-Dual Proximal Splitting." Journal of Imaging 8, no. 6 (May 31, 2022): 157. http://dx.doi.org/10.3390/jimaging8060157.

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Quantitative MRI (qMRI) methods allow reducing the subjectivity of clinical MRI by providing numerical values on which diagnostic assessment or predictions of tissue properties can be based. However, qMRI measurements typically take more time than anatomical imaging due to requiring multiple measurements with varying contrasts for, e.g., relaxation time mapping. To reduce the scanning time, undersampled data may be combined with compressed sensing (CS) reconstruction techniques. Typical CS reconstructions first reconstruct a complex-valued set of images corresponding to the varying contrasts, followed by a non-linear signal model fit to obtain the parameter maps. We propose a direct, embedded reconstruction method for T1ρ mapping. The proposed method capitalizes on a known signal model to directly reconstruct the desired parameter map using a non-linear optimization model. The proposed reconstruction method also allows directly regularizing the parameter map of interest and greatly reduces the number of unknowns in the reconstruction, which are key factors in the performance of the reconstruction method. We test the proposed model using simulated radially sampled data from a 2D phantom and 2D cartesian ex vivo measurements of a mouse kidney specimen. We compare the embedded reconstruction model to two CS reconstruction models and in the cartesian test case also the direct inverse fast Fourier transform. The T1ρ RMSE of the embedded reconstructions was reduced by 37–76% compared to the CS reconstructions when using undersampled simulated data with the reduction growing with larger acceleration factors. The proposed, embedded model outperformed the reference methods on the experimental test case as well, especially providing robustness with higher acceleration factors.

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Hamidpour, Pouria, Alireza Araee, Majid Baniassadi, and Hamid Garmestani. "Multiphase Reconstruction of Heterogamous Materials Using Machine Learning and Quality of Connection Function." Materials 17, no. 13 (June 21, 2024): 3049. http://dx.doi.org/10.3390/ma17133049.

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Establishing accurate structure–property linkages and precise phase volume accuracy in 3D microstructure reconstruction of materials remains challenging, particularly with limited samples. This paper presents an optimized method for reconstructing 3D microstructures of various materials, including isotropic and anisotropic types with two and three phases, using convolutional occupancy networks and point clouds from inner layers of the microstructure. The method emphasizes precise phase representation and compatibility with point cloud data. A stage within the Quality of Connection Function (QCF) repetition loop optimizes the weights of the convolutional occupancy networks model to minimize error between the microstructure’s statistical properties and the reconstructive model. This model successfully reconstructs 3D representations from initial 2D serial images. Comparisons with screened Poisson surface reconstruction and local implicit grid methods demonstrate the model’s efficacy. The developed model proves suitable for high-quality 3D microstructure reconstruction, aiding in structure–property linkages and finite element analysis.

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Qi, Zipeng, Zhengxia Zou, Hao Chen, and Zhenwei Shi. "3D Reconstruction of Remote Sensing Mountain Areas with TSDF-Based Neural Networks." Remote Sensing 14, no. 17 (September 1, 2022): 4333. http://dx.doi.org/10.3390/rs14174333.

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The remote sensing 3D reconstruction of mountain areas has a wide range of applications in surveying, visualization, and game modeling. Different from indoor objects, outdoor mountain reconstruction faces additional challenges, including illumination changes, diversity of textures, and highly irregular surface geometry. Traditional neural network-based methods that lack discriminative features struggle to handle the above challenges, and thus tend to generate incomplete and inaccurate reconstructions. Truncated signed distance function (TSDF) is a commonly used parameterized representation of 3D structures, which is naturally convenient for neural network computation and computer storage. In this paper, we propose a novel deep learning method with TSDF-based representations for robust 3D reconstruction from images containing mountain terrains. The proposed method takes in a set of images captured around an outdoor mountain and produces high-quality TSDF representations of the mountain areas. To address the aforementioned challenges, such as lighting variations and texture diversity, we propose a view fusion strategy based on reweighted mechanisms (VRM) to better integrate multi-view 2D features of the same voxel. A feature enhancement (FE) module is designed for providing better discriminative geometry prior in the feature decoding process. We also propose a spatial–temporal aggregation (STA) module to reduce the ambiguity between temporal features and improve the accuracy of the reconstruction surfaces. A synthetic dataset for reconstructing images containing mountain terrains is built. Our method outperforms the previous state-of-the-art TSDF-based and depth-based reconstruction methods in terms of both 2D and 3D metrics. Furthermore, we collect real-world multi-view terrain images from Google Map. Qualitative results demonstrate the good generalization ability of the proposed method.

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Khan, Muhammad Saif Ullah, Alain Pagani, Marcus Liwicki, Didier Stricker, and Muhammad Zeshan Afzal. "Three-Dimensional Reconstruction from a Single RGB Image Using Deep Learning: A Review." Journal of Imaging 8, no. 9 (August 23, 2022): 225. http://dx.doi.org/10.3390/jimaging8090225.

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Performing 3D reconstruction from a single 2D input is a challenging problem that is trending in literature. Until recently, it was an ill-posed optimization problem, but with the advent of learning-based methods, the performance of 3D reconstruction has also significantly improved. Infinitely many different 3D objects can be projected onto the same 2D plane, which makes the reconstruction task very difficult. It is even more difficult for objects with complex deformations or no textures. This paper serves as a review of recent literature on 3D reconstruction from a single view, with a focus on deep learning methods from 2018 to 2021. Due to the lack of standard datasets or 3D shape representation methods, it is hard to compare all reviewed methods directly. However, this paper reviews different approaches for reconstructing 3D shapes as depth maps, surface normals, point clouds, and meshes; along with various loss functions and metrics used to train and evaluate these methods.

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Xie, Huidong, Hongming Shan, and Ge Wang. "Deep Encoder-Decoder Adversarial Reconstruction (DEAR) Network for 3D CT from Few-View Data." Bioengineering 6, no. 4 (December 9, 2019): 111. http://dx.doi.org/10.3390/bioengineering6040111.

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X-ray computed tomography (CT) is widely used in clinical practice. The involved ionizing X-ray radiation, however, could increase cancer risk. Hence, the reduction of the radiation dose has been an important topic in recent years. Few-view CT image reconstruction is one of the main ways to minimize radiation dose and potentially allow a stationary CT architecture. In this paper, we propose a deep encoder-decoder adversarial reconstruction (DEAR) network for 3D CT image reconstruction from few-view data. Since the artifacts caused by few-view reconstruction appear in 3D instead of 2D geometry, a 3D deep network has a great potential for improving the image quality in a data driven fashion. More specifically, our proposed DEAR-3D network aims at reconstructing 3D volume directly from clinical 3D spiral cone-beam image data. DEAR is validated on a publicly available abdominal CT dataset prepared and authorized by Mayo Clinic. Compared with other 2D deep learning methods, the proposed DEAR-3D network can utilize 3D information to produce promising reconstruction results.

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Yunda Li, Yunda Li, Qi Li Qi Li, Jiaqi Hu Jiaqi Hu, and Yongpeng Zhao Yongpeng Zhao. "Compressive sensing algorithm for 2D reconstruction of THz digital holography." Chinese Optics Letters 13, s1 (2015): S11101–311104. http://dx.doi.org/10.3788/col201513.s11101.

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López-Montes, Alejandro, Pablo Galve, José Manuel Udias, Jacobo Cal-González, Juan José Vaquero, Manuel Desco, and Joaquín L. Herraiz. "Real-Time 3D PET Image with Pseudoinverse Reconstruction." Applied Sciences 10, no. 8 (April 19, 2020): 2829. http://dx.doi.org/10.3390/app10082829.

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Real-time positron emission tomography (PET) may provide information from first-shot images, enable PET-guided biopsies, and allow awake animal studies. Fully-3D iterative reconstructions yield the best images in PET, but they are too slow for real-time imaging. Analytical methods such as Fourier back projection (FBP) are very fast, but yield images of poor quality with artifacts due to noise or data incompleteness. In this work, an image reconstruction based on the pseudoinverse of the system response matrix (SRM) is presented. w. To implement the pseudoinverse method, the reconstruction problem is separated into two stages. First, the axial part of the SRM is pseudo-inverted (PINV) to rebin the 3D data into 2D datasets. Then, the resulting 2D slices can be reconstructed with analytical methods or by applying the pseudoinverse algorithm again. The proposed two-step PINV reconstruction yielded good-quality images at a rate of several frames per second, compatible with real time applications. Furthermore, extremely fast direct PINV reconstruction of projections of the 3D image collapsed along specific directions can be implemented.

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Zhao, Hong Sheng, Ya Xian Wu, and Yun Zhen Wu. "A New Approach to Reconstructing 3D Solid from the Two 2D Views Based on AutoCAD." Applied Mechanics and Materials 88-89 (August 2011): 697–702. http://dx.doi.org/10.4028/www.scientific.net/amm.88-89.697.

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This paper presents a new approach to reconstructing 3D solid model from the two given 2D orthographic views based on AutoCAD, which is much more effective and high efficient for a special kind of compound object than the currently existing ways. The approach emphasizes integrated operating of AutoCAD software and basic knowledge of engineering drawing with no needs for complicated computation and 3D rich imagination and reconstruction. Using this approach, the 3D solid model can be reconstructed effectively and efficiently from the two given 2D orthographic views by several steps of simple operation on computer on the basis of judgment. The research work provides a theoretical possibility for the automatic reconstruction of 3D Solid model of such compound objects, and a simple and practical 3D solid model-reconstructing method for engineers and learners of engineering drawing.

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Röttgen, R., F. Fischbach, M. Plotkin, H. Herzog, T. Freund, R. J. Schröder, and R. Felix. "Colon dissection: a new three‐dimensional reconstruction tool for computed tomography colonography." Acta Radiologica 46, no. 3 (May 2005): 222–26. http://dx.doi.org/10.1080/02841850510020996.

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Purpose: To improve the sensitivity of computed tomography (CT) colonography in the detection of polyps by comparing the 3D reconstruction tool “colon dissection” and endoluminal view (virtual colonoscopy) with axial 2D reconstructions. Material and Methods: Forty‐eight patients (22 M, 26 F, mean age 57±21) were studied after intra‐anal air insufflation in the supine and prone positions using a 16‐slice helical CT (16×0.625 mm, pitch 1.7; detector rotation time 0.5 s; 160 mAs und 120 kV) and conventional colonoscopy. Two radiologists blinded to the results of the conventional colonoscopy analyzed the 3D reconstruction in virtual‐endoscopy mode, in colon‐dissection mode, and axial 2D slices. Results: Conventional colonoscopy revealed a total of 35 polyps in 15 patients; 33 polyps were disclosed by CT methods. Sensitivity and specificity for detecting colon polyps were 94% and 94%, respectively, when using the “colon dissection”, 89% and 94% when using “virtual endoscopy”, and 62% and 100% when using axial 2D reconstruction. Sensitivity in relation to the diameter of colon polyps with “colon dissection”, “virtual colonoscopy”, and axial 2D‐slices was: polyps with a diameter >5.0 mm, 100%, 100%, and 71%, respectively; polyps with a diameter of between 3 and 4.9 mm, 92%, 85%, and 46%; and polyps with a diameter <3 mm, 89%, 78%, and 56%. The difference between “virtual endoscopy” and “colon dissection” in diagnosing polyps up to 4.9 mm in diameter was statistically significant. Conclusion: 3D reconstruction software “colon dissection” improves sensitivity of CT colonography compared with the endoluminal view.

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Brun, Francesco, Luca Brombal, Vittorio Di Trapani, Pasquale Delogu, Sandro Donato, Diego Dreossi, Luigi Rigon, and Renata Longo. "Post-reconstruction 3D single-distance phase retrieval for multi-stage phase-contrast tomography with photon-counting detectors." Journal of Synchrotron Radiation 26, no. 2 (February 11, 2019): 510–16. http://dx.doi.org/10.1107/s1600577519000237.

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In the case of single-distance propagation-based phase-contrast X-ray computed tomography with synchrotron radiation, the conventional reconstruction pipeline includes an independent 2D phase retrieval filtering of each acquired projection prior to the actual reconstruction. In order to compensate for the limited height of the X-ray beam or the small sensitive area of most modern X-ray photon-counting detectors, it is quite common to image large objects with a multi-stage approach, i.e. several acquisitions at different vertical positions of the sample. In this context, the conventional reconstruction pipeline may introduce artifacts at the margins of each vertical stage. This article presents a modified computational protocol where a post-reconstruction 3D volume phase retrieval is applied. By comparing the conventional 2D and the proposed 3D reconstructions of a large mastectomy specimen (9 cm in diameter and 3 cm in height), it is here shown that the 3D approach compensates for the multi-stage artifacts, it avoids refined projection stitching, and the image quality in terms of spatial resolution, contrast and contrast-to-noise ratio is preserved.

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Tang, Yanlong, Yun Zhang, Xiaoguang Han, Fang-Lue Zhang, Yu-Kun Lai, and Ruofeng Tong. "3D corrective nose reconstruction from a single image." Computational Visual Media 8, no. 2 (December 6, 2021): 225–37. http://dx.doi.org/10.1007/s41095-021-0237-5.

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AbstractThere is a steadily growing range of applications that can benefit from facial reconstruction techniques, leading to an increasing demand for reconstruction of high-quality 3D face models. While it is an important expressive part of the human face, the nose has received less attention than other expressive regions in the face reconstruction literature. When applying existing reconstruction methods to facial images, the reconstructed nose models are often inconsistent with the desired shape and expression. In this paper, we propose a coarse-to-fine 3D nose reconstruction and correction pipeline to build a nose model from a single image, where 3D and 2D nose curve correspondences are adaptively updated and refined. We first correct the reconstruction result coarsely using constraints of 3D-2D sparse landmark correspondences, and then heuristically update a dense 3D-2D curve correspondence based on the coarsely corrected result. A final refinement step is performed to correct the shape based on the updated 3D-2D dense curve constraints. Experimental results show the advantages of our method for 3D nose reconstruction over existing methods.

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Allouche, Amiram, and Arie Feuer. "2D motion aided sampling and reconstruction." Journal of Visual Communication and Image Representation 20, no. 1 (January 2009): 1–8. http://dx.doi.org/10.1016/j.jvcir.2008.09.005.

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Song, Yuqing. "Boundary fitting for 2D curve reconstruction." Visual Computer 26, no. 3 (October 22, 2009): 187–204. http://dx.doi.org/10.1007/s00371-009-0395-4.

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Nan, Liangliang, Andrei Sharf, and Baoquan Chen. "2D-D Lifting for Shape Reconstruction." Computer Graphics Forum 33, no. 7 (October 2014): 249–58. http://dx.doi.org/10.1111/cgf.12493.

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Hirano, Daisuke, Yusuke Funayama, and Takashi Maekawa. "3D Shape Reconstruction from 2D Images." Computer-Aided Design and Applications 6, no. 5 (January 2009): 701–10. http://dx.doi.org/10.3722/cadaps.2009.701-710.

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Xie, L., and R. Wang. "AUTOMATIC INDOOR BUILDING RECONSTRUCTION FROM MOBILE LASER SCANNING DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 12, 2017): 417–22. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-417-2017.

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Indoor reconstruction from point clouds is a hot topic in photogrammetry, computer vision and computer graphics. Reconstructing indoor scene from point clouds is challenging due to complex room floorplan and line-of-sight occlusions. Most of existing methods deal with stationary terrestrial laser scanning point clouds or RGB-D point clouds. In this paper, we propose an automatic method for reconstructing indoor 3D building models from mobile laser scanning point clouds. The method includes 2D floorplan generation, 3D building modeling, door detection and room segmentation. The main idea behind our approach is to separate wall structure into two different types as the inner wall and the outer wall based on the observation of point distribution. Then we utilize a graph cut based optimization method to solve the labeling problem and generate the 2D floorplan based on the optimization result. Subsequently, we leverage an &alpha;-shape based method to detect the doors on the 2D projected point clouds and utilize the floorplan to segment the individual room. The experiments show that this door detection method can achieve a recognition rate at 97% and the room segmentation method can attain the correct segmentation results. We also evaluate the reconstruction accuracy on the synthetic data, which indicates the accuracy of our method is comparable to the state-of-the art.

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Wu, Feng He, Jin Fen Wang, Jun Wang, and Yu Bo Ren. "3D Surface Reconstruction from Single 2D Image." Key Engineering Materials 522 (August 2012): 886–90. http://dx.doi.org/10.4028/www.scientific.net/kem.522.886.

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The image-based 3D reconstruction technique is an important method of 3D data passive acquired in computer vision. Aimed at the limitation of traditional SFS based 3D surface reconstruction method, a combined image-based 3D surface reconstruction method is given in this paper. Firstly, the pretreatment method of gradation is applied to convert the input color image to 256 gray image; secondly, shape from shading method is applied to recover the 3D profile of object; finally, the B-spline technique is adopted to reconstruct the model of surface and improve the resolution of surface. The key techniques such as the principle and algorithm of B-spline interpolation reconstruction are analyzed, and the experimental results are also given. The single-image based 3D reconstruction method given in this paper will supply a helpful condition for the extensive used of SFS technique.

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Quan, Bao, and Jiang Nan. "A Simplified 3D Reconstruction Technique for Tomographic Particle Image Velocimetry." Advanced Materials Research 718-720 (July 2013): 2184–90. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.2184.

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Tomographic particle image velocimetry (Tomo-PIV) is a newly developed technique for three-component three-dimensional (3C-3D) velocity measurement based on the tomographic reconstruction of a 3D volume light intensity field from multiple two-dimensional projections. A simplification of 3D tomographic reconstruction model, which reduced from a 3D volume with 2D images to a 2D slice with 1D lines, simplify this 3D reconstruction into a problem of 2D plane reconstruction by means of optical tomography, is applied in this paper . The principles and details of the tomographic algorithm are discussed, as well as the study of ART and MART reconstruction algorithm is carried out by means of computer-simulated image reconstruction procedure. The three-dimensional volume particle field is reconstructed by MART reconstruction algorithm base on the simplified 3D reconstruction model which made a high reconstruction quality Q=81.37% prove that the way of simplification by MART reconstruction is feasible, so it could be applied in reconstruction of 3D particle field in tomographic particle image velocimetry system.

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Jeong, Sunwon, and Ju Yong Chang. "MeshLifter: Weakly Supervised Approach for 3D Human Mesh Reconstruction from a Single 2D Pose Based on Loop Structure." Sensors 20, no. 15 (July 30, 2020): 4257. http://dx.doi.org/10.3390/s20154257.

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In this paper, we address the problem of 3D human mesh reconstruction from a single 2D human pose based on deep learning. We propose MeshLifter, a network that estimates a 3D human mesh from an input 2D human pose. Unlike most existing 3D human mesh reconstruction studies that train models using paired 2D and 3D data, we propose a weakly supervised learning method based on a loop structure to train the MeshLifter. The proposed method alleviates the difficulty of obtaining ground-truth 3D data to ensure that the MeshLifter can be trained successfully from a 2D human pose dataset and an unpaired 3D motion capture dataset. We compare the proposed method with recent state-of-the-art studies through various experiments and show that the proposed method achieves effective 3D human mesh reconstruction performance. Notably, our proposed method achieves a reconstruction error of 59.1 mm without using the 3D ground-truth data of Human3.6M, the standard dataset for 3D human mesh reconstruction.

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Xu, Ya, Fangzhou Nan, Weiping Cao, Song Huang, and Tianyao Hao. "Gravity anomaly reconstruction based on nonequispaced Fourier transform." GEOPHYSICS 84, no. 6 (November 1, 2019): G83—G92. http://dx.doi.org/10.1190/geo2018-0683.1.

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Irregular sampled gravity data are often interpolated into regular grid data for convenience of data processing and interpretation. The compressed sensing theory provides a signal reconstruction method that can recover a sparse signal from far fewer samples. We have introduced a gravity data reconstruction method based on the nonequispaced Fourier transform (NFT) in the framework of compressed sensing theory. We have developed a sparsity analysis and a reconstruction algorithm with an iterative cooling thresholding method and applied to the gravity data of the Bishop model. For 2D data reconstruction, we use two methods to build the weighting factors: the Gaussian function and the Voronoi method. Both have good reconstruction results from the 2D data tests. The 2D reconstruction tests from different sampling rates and comparison with the minimum curvature and the kriging methods indicate that the reconstruction method based on the NFT has a good reconstruction result even with few sampling data.

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Zhang, Hongtao, Yuki Shinomiya, and Shinichi Yoshida. "3D MRI Reconstruction Based on 2D Generative Adversarial Network Super-Resolution." Sensors 21, no. 9 (April 23, 2021): 2978. http://dx.doi.org/10.3390/s21092978.

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The diagnosis of brain pathologies usually involves imaging to analyze the condition of the brain. Magnetic resonance imaging (MRI) technology is widely used in brain disorder diagnosis. The image quality of MRI depends on the magnetostatic field strength and scanning time. Scanners with lower field strengths have the disadvantages of a low resolution and high imaging cost, and scanning takes a long time. The traditional super-resolution reconstruction method based on MRI generally states an optimization problem in terms of prior information. It solves the problem using an iterative approach with a large time cost. Many methods based on deep learning have emerged to replace traditional methods. MRI super-resolution technology based on deep learning can effectively improve MRI resolution through a three-dimensional convolutional neural network; however, the training costs are relatively high. In this paper, we propose the use of two-dimensional super-resolution technology for the super-resolution reconstruction of MRI images. In the first reconstruction, we choose a scale factor of 2 and simulate half the volume of MRI slices as input. We utilize a receiving field block enhanced super-resolution generative adversarial network (RFB-ESRGAN), which is superior to other super-resolution technologies in terms of texture and frequency information. We then rebuild the super-resolution reconstructed slices in the MRI. In the second reconstruction, the image after the first reconstruction is composed of only half of the slices, and there are still missing values. In our previous work, we adopted the traditional interpolation method, and there was still a gap in the visual effect of the reconstructed images. Therefore, we propose a noise-based super-resolution network (nESRGAN). The noise addition to the network can provide additional texture restoration possibilities. We use nESRGAN to further restore MRI resolution and high-frequency information. Finally, we achieve the 3D reconstruction of brain MRI images through two super-resolution reconstructions. Our proposed method is superior to 3D super-resolution technology based on deep learning in terms of perception range and image quality evaluation standards.

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Schell, Adam, John M. Rhee, John Holbrook, Eric Lenehan, and Kun Young Park. "Assessing Foraminal Stenosis in the Cervical Spine: A Comparison of Three-Dimensional Computed Tomographic Surface Reconstruction to Two-Dimensional Modalities." Global Spine Journal 7, no. 3 (April 11, 2017): 266–71. http://dx.doi.org/10.1177/2192568217699190.

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Study Design: Retrospective radiographic study. Objective: The optimal radiographic modality for assessing cervical foraminal stenosis is unclear. Determination on conventional axial cuts is made difficult due in part to the complex, oblique orientation of the cervical neuroforamen. The utility of 3-dimensonal (3D) computed tomography (CT) reconstruction in improving neuroforaminal assessment is not well understood. The objective of this study is to determine inter-rater variability in grading cervical foraminal stenosis using 3 different CT imaging modalities: 3D CT surface reconstructions (3DSR), 2D sagittal oblique multiplanar reformations (2D-SOMPR), and conventional 2D axial CT imaging. Methods: Pretreatment CT scans of 25 patients undergoing surgery for cervical spondylotic radiculopathy were analyzed at 2 levels: C5-C6 and C6-C7. Simple interrater agreement and kappa-Fleiss coefficients were calculated for each imaging modality and stenosis grade. Image reviewers (attending spine surgeon, attending neuroradiologist, spine fellow) interpreted each CT scan in 3 different formats: axial, 2D-SOMPR, and 3DSR. Four cervical foramina at 2 spinal levels were graded as normal (no stenosis), mild (≤25% stenosis), moderate (25%-50% stenosis), or severe (>50% stenosis). Results: Across all imaging modalities, interrater reliability was fair when grading foraminal stenosis (κ < 0.4). Agreement was lowest for the axial images (κ = 0.119) and highest for the 3D CT reconstructions (κ = 0.334). 2D-SOMPR images also led to improved interrater reliability when compared with axial images (κ = 0.255). Conclusion: Grading cervical foraminal stenosis using conventional axial CT imaging is difficult with low interrater reliability. CT modalities that provide a circumferential view of the cervical foramen, such as 2D-SOMPR and 3D CT reconstruction, had higher rates of interobserver reliability in grading foraminal stenosis than conventional axial cuts, with 3D having the highest. As these 3D reconstructions can be obtained at no additional cost or radiation exposure over a conventional CT scan, and because they can provide useful information in determining levels being considered for surgical decompression, we recommend they be utilized when evaluating cervical foramina.

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Pawelec, Iga, Michael Wakin, and Paul Sava. "Missing trace reconstruction for 2D land seismic data with randomized sparse sampling." GEOPHYSICS 86, no. 3 (May 1, 2021): P25—P36. http://dx.doi.org/10.1190/geo2020-0683.1.

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Acquisition of high-quality land seismic data requires (expensive) dense source and receiver geometries to avoid aliasing-related problems. Alternatively, acquisition using the concept of compressive sensing (CS) allows for similarly high-quality land seismic data using fewer measurements provided that the designed geometry and sparse recovery strategy are well matched. We have developed a complex wavelet-based sparsity-promoting wavefield reconstruction strategy to overcome challenges in land seismic data interpolation using the CS framework. Despite having lower angular sensitivity than curvelets, complex wavelets improve the reconstruction of sparsely acquired land data while being faster and requiring less storage. Unlike the Fourier transform, the complex wavelet transform localizes aliasing-related artifacts likely to be present in field data and yields reconstructions with fewer artifacts and higher signal-to-noise ratios. We determine that the data recovery success depends on the number and the geometry of the missing traces as revealed by analyzing reconstructions from multiple realizations of trace geometry and data decimation ratios. Using half the number of traces required by the regular sampling rules and thus reducing the acquisition costs, we find that data are appropriately reconstructed provided that there are no large gaps in the strategic places.

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Brewin, Mark A., and David G. Kerwin. "Accuracy of Scaling and DLT Reconstruction Techniques for Planar Motion Analyses." Journal of Applied Biomechanics 19, no. 1 (February 2003): 79–88. http://dx.doi.org/10.1123/jab.19.1.79.

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Numerous planar analyses of sports activities have utilized scaling techniques to convert image coordinates into real-space locations. While in certain circumstances, such as competition, the camera must be elevated above the activity and its tilt accounted for, the influence of tilt on reconstruction accuracy using scaling is currently unreported. A modification of the direct linear transformation (2D-DLT) which considers only the vertical plane provides an alternative approach for planar reconstruction. This study compared the reconstruction accuracy between scaling and 2D-DLT over a range of tilt angles throughout a 6-m horizontal field of view. Four calibration and 30 reconstruction markers of known locations in a vertical plane were videotaped from nine positions to provide tilt angles varying between –2° and +6°. Both techniques were used to estimate real-space locations for the reconstruction markers, and accuracy was calculated by comparing known and reconstructed locations. The smallest reconstruction errors were obtained using 2D-DLT and were unaffected by camera tilt. The scaling technique produced significantly larger (p < 0.01) errors than 2D-DLT, with the exception of 0° and +1° of tilt, and there was a detrimental effect on accuracy as the magnitude of tilt increased. The largest variations in reconstruction errors were associated with scaling, with markers at the extremes of the image showing the largest errors. The 2D-DLT approach provided accurate reconstruction data for planar analyses across the field of view and throughout the range of tilt angles, and should be preferred over scaling techniques.

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Mouton, Isabelle, Shyam Katnagallu, Surendra Kumar Makineni, Oana Cojocaru-Mirédin, Torsten Schwarz, Leigh Thomas Stephenson, Dierk Raabe, and Baptiste Gault. "Calibration of Atom Probe Tomography Reconstructions Through Correlation with Electron Micrographs." Microscopy and Microanalysis 25, no. 2 (February 4, 2019): 301–8. http://dx.doi.org/10.1017/s1431927618016161.

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AbstractAlthough atom probe tomography (APT) reconstructions do not directly influence the local elemental analysis, any structural inferences from APT volumes demand a reliable reconstruction of the point cloud. Accurate estimation of the reconstruction parameters is crucial to obtain reliable spatial scaling. In the current work, a new automated approach of calibrating atom probe reconstructions is developed using only one correlative projection electron microscopy (EM) image. We employed an algorithm that implements a 2D cross-correlation of microstructural features observed in both the APT reconstructions and the corresponding EM image. We apply this protocol to calibrate reconstructions in a Cu(In,Ga)Se2-based semiconductor and in a Co-based superalloy. This work enables us to couple chemical precision to structural information with relative ease.

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Lium, Ola, Yong Bin Kwon, Antonios Danelakis, and Theoharis Theoharis. "Robust 3D Face Reconstruction Using One/Two Facial Images." Journal of Imaging 7, no. 9 (August 30, 2021): 169. http://dx.doi.org/10.3390/jimaging7090169.

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Being able to robustly reconstruct 3D faces from 2D images is a topic of pivotal importance for a variety of computer vision branches, such as face analysis and face recognition, whose applications are steadily growing. Unlike 2D facial images, 3D facial data are less affected by lighting conditions and pose. Recent advances in the computer vision field have enabled the use of convolutional neural networks (CNNs) for the production of 3D facial reconstructions from 2D facial images. This paper proposes a novel CNN-based method which targets 3D facial reconstruction from two facial images, one in front and one from the side, as are often available to law enforcement agencies (LEAs). The proposed CNN was trained on both synthetic and real facial data. We show that the proposed network was able to predict 3D faces in the MICC Florence dataset with greater accuracy than the current state-of-the-art. Moreover, a scheme for using the proposed network in cases where only one facial image is available is also presented. This is achieved by introducing an additional network whose task is to generate a rotated version of the original image, which in conjunction with the original facial image, make up the image pair used for reconstruction via the previous method.

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Zhang, Fenghao, Lin Zhao, Shengling Li, Wanjuan Su, Liman Liu, and Wenbing Tao. "3D hand pose and shape estimation from monocular RGB via efficient 2D cues." Computational Visual Media 10, no. 1 (February 2023): 79–96. http://dx.doi.org/10.1007/s41095-023-0346-4.

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AbstractEstimating 3D hand shape from a single-view RGB image is important for many applications. However, the diversity of hand shapes and postures, depth ambiguity, and occlusion may result in pose errors and noisy hand meshes. Making full use of 2D cues such as 2D pose can effectively improve the quality of 3D human hand shape estimation. In this paper, we use 2D joint heatmaps to obtain spatial details for robust pose estimation. We also introduce a depth-independent 2D mesh to avoid depth ambiguity in mesh regression for efficient hand-image alignment. Our method has four cascaded stages: 2D cue extraction, pose feature encoding, initial reconstruction, and reconstruction refinement. Specifically, we first encode the image to determine semantic features during 2D cue extraction; this is also used to predict hand joints and for segmentation. Then, during the pose feature encoding stage, we use a hand joints encoder to learn spatial information from the joint heatmaps. Next, a coarse 3D hand mesh and 2D mesh are obtained in the initial reconstruction step; a mesh squeeze-and-excitation block is used to fuse different hand features to enhance perception of 3D hand structures. Finally, a global mesh refinement stage learns non-local relations between vertices of the hand mesh from the predicted 2D mesh, to predict an offset hand mesh to fine-tune the reconstruction results. Quantitative and qualitative results on the FreiHAND benchmark dataset demonstrate that our approach achieves state-of-the-art performance.

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Ximen, Jiye. "Analytical Self-Consistent Theory of Three Dimensional Image Reconstruction from Projections." Proceedings, annual meeting, Electron Microscopy Society of America 48, no. 1 (August 12, 1990): 456–57. http://dx.doi.org/10.1017/s0424820100181038.

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Image reconstruction techniques have been widely applied to electron microscopy and computerized tomography. The classic algebraic reconstruction problem is to reconstruct the three dimensional (3D) objects from experimentally measured two dimensional (2D) projections. In literature, only approximate algorithm and iterative procedure have been investigated. Recently, an analytical self-consistent reconstruction method has been proposed by the author. A discrete/continuous object is given:whlie 2D projections are

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Senhaji, Mohammed, and Rachid Benslimane. "3D Reconstruction of Moroccan-Andalusian Muqarnas Domes." Journal on Computing and Cultural Heritage 15, no. 2 (June 30, 2022): 1–27. http://dx.doi.org/10.1145/3479712.

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Muqarnas is a three-dimensional Islamic architecture decoration conceived as a spatial arrangement of 3D shapes according to precise geometric rules. Muqarnas work can be performed on several types of materials such as stone, wood, plaster or brick. This paper aims to propose an algorithmic method to construct the circular Moroccan-Andalusian Muqarnas Ornaments from their 2D representation models. The principle is to extract from a 2D design geometric information and underlying rules useful for the construction of its corresponding 3D model. To do so, the first stage consists to determine for each 2D shape of the 2D design model its corresponding 3D Muqarnas element. The second stage consists to perform the spatial composition of these identified 3D elements by using their neighborhood properties. The performance of the proposed method is evaluated by its capacity to reconstruct historical circular Muqarnas ornaments appeared during the Almoravids (1040-1147) and Almohads periods (1147-1269). This performance is further evaluated by the ability of the proposed method to construct all possible variants of a historical 3D Muqarnas ornament by considering the different 3D shapes which can be associated to the same 2D shape.

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Zhang, Dong, Yongshun Zhang, and Cunqian Feng. "Joint-2D-SL0 Algorithm for Joint Sparse Matrix Reconstruction." International Journal of Antennas and Propagation 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/6862852.

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Sparse matrix reconstruction has a wide application such as DOA estimation and STAP. However, its performance is usually restricted by the grid mismatch problem. In this paper, we revise the sparse matrix reconstruction model and propose the joint sparse matrix reconstruction model based on one-order Taylor expansion. And it can overcome the grid mismatch problem. Then, we put forward the Joint-2D-SL0 algorithm which can solve the joint sparse matrix reconstruction problem efficiently. Compared with the Kronecker compressive sensing method, our proposed method has a higher computational efficiency and acceptable reconstruction accuracy. Finally, simulation results validate the superiority of the proposed method.

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Wen, Fanbiao, and Qingguang Li. "FAmesh: Generating Frequency Adaptive Meshes from Single Images under 2D Hole Constraints." Applied Sciences 13, no. 10 (May 13, 2023): 5995. http://dx.doi.org/10.3390/app13105995.

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Reconstructing 3D models from a single image has numerous applications in fields such as VR/AR, medical imaging, and gaming. However, most mesh-based methods are limited by the use of 0-genus initial templates, which makes it difficult to reconstruct 3D meshes with complex topologies. Additionally, existing methods often prioritize reconstructing the overall shape and neglect to study local meshes with varying curvatures, resulting in a lack of correct and detailed local features in the generated meshes. This paper proposes a 3D reconstruction framework that transitions from global to local and incorporates MLP and GCN. The framework introduces a mesh pruning strategy under a 2D hole constraint to restore the correct mesh topology. Moreover, the framework fine-tunes local details by separately learning corresponding mapping functions on high-frequency and low-frequency local extended patches. The experiment with the proposed network on the ShapeNet dataset shows that the network has a CD value of 1.763 and an F-score of 85.40. The results from extensive experiments demonstrate that our proposed method outperforms existing methods in topology correction and local detail reconstruction.

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Nadkarni, Rohan, Darin P. Clark, Alex J. Allphin, and Cristian T. Badea. "A Deep Learning Approach for Rapid and Generalizable Denoising of Photon-Counting Micro-CT Images." Tomography 9, no. 4 (July 2, 2023): 1286–302. http://dx.doi.org/10.3390/tomography9040102.

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Photon-counting CT (PCCT) is powerful for spectral imaging and material decomposition but produces noisy weighted filtered backprojection (wFBP) reconstructions. Although iterative reconstruction effectively denoises these images, it requires extensive computation time. To overcome this limitation, we propose a deep learning (DL) model, UnetU, which quickly estimates iterative reconstruction from wFBP. Utilizing a 2D U-net convolutional neural network (CNN) with a custom loss function and transformation of wFBP, UnetU promotes accurate material decomposition across various photon-counting detector (PCD) energy threshold settings. UnetU outperformed multi-energy non-local means (ME NLM) and a conventional denoising CNN called UnetwFBP in terms of root mean square error (RMSE) in test set reconstructions and their respective matrix inversion material decompositions. Qualitative results in reconstruction and material decomposition domains revealed that UnetU is the best approximation of iterative reconstruction. In reconstructions with varying undersampling factors from a high dose ex vivo scan, UnetU consistently gave higher structural similarity (SSIM) and peak signal-to-noise ratio (PSNR) to the fully sampled iterative reconstruction than ME NLM and UnetwFBP. This research demonstrates UnetU’s potential as a fast (i.e., 15 times faster than iterative reconstruction) and generalizable approach for PCCT denoising, holding promise for advancing preclinical PCCT research.

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Vallet, B., B. Soheilian, and M. Brédif. "Combinatorial clustering and Its Application to 3D Polygonal Traffic Sign Reconstruction From Multiple Images." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-3 (August 7, 2014): 165–72. http://dx.doi.org/10.5194/isprsannals-ii-3-165-2014.

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The 3D reconstruction of similar 3D objects detected in 2D faces a major issue when it comes to grouping the 2D detections into clusters to be used to reconstruct the individual 3D objects. Simple clustering heuristics fail as soon as similar objects are close. This paper formulates a framework to use the geometric quality of the reconstruction as a hint to do a proper clustering. We present a methodology to solve the resulting combinatorial optimization problem with some simplifications and approximations in order to make it tractable. The proposed method is applied to the reconstruction of 3D traffic signs from their 2D detections to demonstrate its capacity to solve ambiguities.

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Zhang, Nengyu. "A new Electron Microscopy tomography: Least squares pseudoimage reconstruction technique." Proceedings, annual meeting, Electron Microscopy Society of America 49 (August 1991): 538–39. http://dx.doi.org/10.1017/s0424820100087008.

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The least square pseudoimage (LSP) Reconstruction technique is based on matrix inversion. Due to the absence of information in electron microscopy, usually in an angle range from 60° to 90°, the matrix is degraded. The degraded matrix is used to compute two-dimensional (2D) projections of the three-dimensional (3D) object along the Z-axis (direction of the electron beam) and tilted around the Y-axis (tilt axis). Applying the pseudoinverse of the degraded matrix to electron micrographs, which are 2D projections of the 3D object, gives the pseudoimage.Since all of the slices which are perpendicular to the Y axis are related to the same degraded matrix, the 3D reconstruction problem can be simplified as series 2D reconstruction problems. All of the 2D pseudoimages are formed by the same pseudoinverse of a degraded matrix. In this case, however, the degraded matrix projects a 2D object onto an ID projection along the Z axis. Each slice is computed from a single line in all of the electron micrographs.

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Godino-Moya, Alejandro, Rosa-María Menchón-Lara, Marcos Martín-Fernández, Claudia Prieto, and Carlos Alberola-López. "Elastic AlignedSENSE for Dynamic MR Reconstruction: A Proof of Concept in Cardiac Cine." Entropy 23, no. 5 (April 29, 2021): 555. http://dx.doi.org/10.3390/e23050555.

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Numerous methods in the extensive literature on magnetic resonance imaging (MRI) reconstruction exploit temporal redundancy to accelerate cardiac cine. Some of them include motion compensation, which involves high computational costs and long runtimes. In this work, we proposed a method—elastic alignedSENSE (EAS)—for the direct reconstruction of a motion-free image plus a set of nonrigid deformations to reconstruct a 2D cardiac sequence. The feasibility of the proposed approach was tested in 2D Cartesian and golden radial multi-coil breath-hold cardiac cine acquisitions. The proposed approach was compared against parallel imaging compressed sense (sPICS) and group-wise motion corrected compressed sense (GWCS) reconstructions. EAS provides better results on objective measures with considerable less runtime when an acceleration factor is higher than 10×. Subjective assessment of an expert, however, invited proposing the combination of EAS and GWCS as a preferable alternative to GWCS or EAS in isolation.

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Qi, Haoyuan, Hafeesudeen Sahabudeen, Baokun Liang, Miroslav Položij, Matthew A. Addicoat, Tatiana E. Gorelik, Mike Hambsch, et al. "Near–atomic-scale observation of grain boundaries in a layer-stacked two-dimensional polymer." Science Advances 6, no. 33 (August 2020): eabb5976. http://dx.doi.org/10.1126/sciadv.abb5976.

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Two-dimensional (2D) polymers hold great promise in the rational materials design tailored for next-generation applications. However, little is known about the grain boundaries in 2D polymers, not to mention their formation mechanisms and potential influences on the material’s functionalities. Using aberration-corrected high-resolution transmission electron microscopy, we present a direct observation of the grain boundaries in a layer-stacked 2D polyimine with a resolution of 2.3 Å, shedding light on their formation mechanisms. We found that the polyimine growth followed a “birth-and-spread” mechanism. Antiphase boundaries implemented a self-correction to the missing-linker and missing-node defects, and tilt boundaries were formed via grain coalescence. Notably, we identified grain boundary reconstructions featuring closed rings at tilt boundaries. Quantum mechanical calculations revealed that boundary reconstruction is energetically allowed and can be generalized into different 2D polymer systems. We envisage that these results may open up the opportunity for future investigations on defect-property correlations in 2D polymers.

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Saharan, Ravi, and ChoudharyVijaypal Singh. "Reassembly of 2D Fragments in Image Reconstruction." International Journal of Computer Applications 19, no. 5 (2011): 41–45. http://dx.doi.org/10.5120/2354-3077.

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Ohrhallinger, S., J. Peethambaran, A. D. Parakkat, T. K. Dey, and R. Muthuganapathy. "2D Points Curve Reconstruction Survey and Benchmark." Computer Graphics Forum 40, no. 2 (May 2021): 611–32. http://dx.doi.org/10.1111/cgf.142659.

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Kontrym-Sznajd, G. "Momentum Density Reconstruction from 2D ACAR Data." Materials Science Forum 105-110 (January 1992): 325–32. http://dx.doi.org/10.4028/www.scientific.net/msf.105-110.325.

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Attar, Rahman, Xiang Xie, Zhihua Wang, and Shigang Yue. "2D reconstruction of small intestine's interior wall." Computers in Biology and Medicine 105 (February 2019): 54–63. http://dx.doi.org/10.1016/j.compbiomed.2018.12.001.

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Bisiacco, M. "On the state reconstruction of 2D systems." Systems & Control Letters 5, no. 5 (April 1985): 347–53. http://dx.doi.org/10.1016/0167-6911(85)90033-7.

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Journal articles on the topic 'Reconstruction 2D'

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