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Journal articles on the topic 'Reconstruction 2D'

Author: Grafiati

Published: 25 January 2025

Last updated: 31 July 2025

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1

Van Houten, Elijah E. W., Sajad Ghazavi, Guillaume Fle, et al. "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 (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 distributio

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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 (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 fr

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Zhu, Linqi, Chong Zhang, Chaomo Zhang, et al. "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 st

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

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V, PremaLatha, Dinesh Kumar Anguraj, and Nikhat Parveen. "Integrated Neural-Hybrid System for Efficient Tumor Detection and Object Reconstruction." Data and Metadata 4 (April 16, 2025): 850. https://doi.org/10.56294/dm2025850.

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In computer vision and robotics, reconstructing multi-view 3D images is essential for accurate object representation from 2D data. In the first study, optimised weights through Adaptive School of Fish Optimisation are combined with 2D and 3D networks to introduce a Residual Network-50 model for deep learning-based 3D image reconstruction. On the ShapeNet dataset, this method demonstrates superior accuracy (0.993), F-score (0.734), and IoU (99.3%). Using Concurrent Excited DenseNet (CED)for feature extraction and Attention-Dense GRUs for prediction, the second study introduces the Concurrent At

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Cardoen, Thorsten, Sam Leroux, and Pieter Simoens. "Iterative Online 3D Reconstruction from RGB Images." Sensors 22, no. 24 (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

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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 (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, an

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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 obtaine

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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 (1997): 369–74. http://dx.doi.org/10.1016/s0168-9002(97)00321-5.

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11

Jiang, Wen. "Helicon: helical reconstruction from a single 2D image." Structural Dynamics 12, no. 2_Supplement (2025): A292. https://doi.org/10.1063/4.0000598.

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Helical symmetry is a common structural feature of many biological macromolecules, such as cytoskeletons, viruses, and pathological amyloid fibrils, that play diverse roles in cellular functions and diseases. Helical symmetry offers a unique advantage for electron microscopy (EM) imaging and 3D reconstruction: different views can be obtained along the helical axis in a single image viewed from the side. This property enabled the first three-dimensional (3D) EM reconstructions of helical structures, such as tobacco mosaic virus (TMV) and bacteriophage T4 tail in the late 1960s. However, helical

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Hanhela, Matti, Antti Paajanen, Mikko J. Nissi та Ville Kolehmainen. "Embedded Quantitative MRI T1ρ Mapping Using Non-Linear Primal-Dual Proximal Splitting". Journal of Imaging 8, № 6 (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,

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Visutsak, Porawat, Xiabi Liu, Chalothon Choothong, and Fuangfar Pensiri. "SIFT-Based Depth Estimation for Accurate 3D Reconstruction in Cultural Heritage Preservation." Applied System Innovation 8, no. 2 (2025): 43. https://doi.org/10.3390/asi8020043.

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This paper describes a proposed method for preserving tangible cultural heritage by reconstructing a 3D model of cultural heritage using 2D captured images. The input data represent a set of multiple 2D images captured using different views around the object. An image registration technique is applied to configure the overlapping images with the depth of images computed to construct the 3D model. The automatic 3D reconstruction system consists of three steps: (1) Image registration for managing the overlapping of 2D input images; (2) Depth computation for managing image orientation and calibra

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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 (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 (QC

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Sun, Tianze, Blessed Kondowe, Brave Kadoko Nyirenda, Jun Liu, Hui Zhang, and Jin Shang. "Experimental comparative study of Two-dimensional and Three-dimensional CT reconstruction in detecting maxillofacial fractures at Mzuzu Central Hospital, Malawi." Malawi Medical Journal 36, no. 5 (2025): 303–7. https://doi.org/10.4314/mmj.v36i5.2.

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ObjectiveThe aim of this study is to compare the diagnostic value of two-dimensional (2D) CT and three-dimensional (3D) CT reconstruction techniques in detecting maxillofacial fractures in patients at Mzuzu Central Hospital (MCH).Methods 67 maxillofacial trauma patients admitted to Mzuzu Central Hospital from Jan to Sep 2024 underwent multi-slice spiral CT (MSCT) scanning. Images were post-processed using 2D and 3D reconstruction techniques. Clinical and radiological data were collected from the patients, and a comparative analysis of the results from the two reconstruction techniques was perf

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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 (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 o

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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 (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

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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 (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,

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Panicker, Prasanth, Ashford Lidiya George, Rajana Mohan Kumar, Mandapathi Srujan Kumar, Feby Francis, and Deepak Peter. "Efficiency of 2D versus 3D CT scans in maxillofacial trauma: A retrospective study." Bioinformation 21, no. 05 (2025): 985–89. https://doi.org/10.6026/973206300210985.

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The transition from 2D CT slices to 3D CT volume rendering reconstructions has significantly improved the precision of the trauma diagnoses and broadened the range of potential treatments. Using a 64-slice CT scanner, the fracture detection score and fracture comparative score were used to compare 2D and 3D fracture cases for detection and diagnosis. The study comprised 200 maxillofacial fracture cases. 2D CT cuts detected 100% of fractures, but 3D cuts missed 4%. In 66.66% of fracture score combinations, 2D CT slices could improve diagnosis. This study showed that 2D CT cuts are better at fra

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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 op

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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 (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

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Röttgen, R., F. Fischbach, M. Plotkin, et al. "Colon dissection: a new three‐dimensional reconstruction tool for computed tomography colonography." Acta Radiologica 46, no. 3 (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 analy

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López-Montes, Alejandro, Pablo Galve, José Manuel Udias, et al. "Real-Time 3D PET Image with Pseudoinverse Reconstruction." Applied Sciences 10, no. 8 (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 prob

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Brun, Francesco, Luca Brombal, Vittorio Di Trapani, et al. "Post-reconstruction 3D single-distance phase retrieval for multi-stage phase-contrast tomography with photon-counting detectors." Journal of Synchrotron Radiation 26, no. 2 (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 recons

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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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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 discuss

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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 (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 (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 (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 (2009): 701–10. http://dx.doi.org/10.3722/cadaps.2009.701-710.

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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 tec

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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 (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 reconst

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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 (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

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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

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Siddiqua, Maria, and Muhammad Furqan Zia. "A Robust Framework for 2D Human Face Reconstruction from Half-Frontal Views in Low-Quality Surveillance Footage." International Journal of Emerging Engineering and Technology 3, no. 2 (2024): 13–18. https://doi.org/10.57041/z5e98x92.

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This paper proposes a robust framework for reconstructing 2D human facial images from half-frontal views, primarily captured under low-quality surveillance conditions. A custom MATLAB-based Graphical User Interface (GUI) is developed to support the complete pipeline, including frame extraction, enhancement, and face reconstruction. Representative frames are extracted and enhanced for video inputs using one of three techniques: histogram equalization, contrast stretching, or logarithmic transformation. Reconstruction involves detecting a single eye from the half-frontal image, followed by horiz

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Zheng, Lin, Wang Chenghao, Li Zichao, Wang Zhuoyue, Liu Xinqi, and Zhu Yue. "Neural Radiance Fields Convert 2D to 3D Texture." Applied Science and Biotechnology Journal for Advanced Research 3, no. 3 (2024): 40–44. https://doi.org/10.5281/zenodo.12200107.

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The objective of our project is to capture pictures or videos by surrounding a circle of objects, such as chairs, tables, cars, and more.[1]Utilizing advanced 3D reconstruction technology, we aim to generate 3D models of these captured objects. Post reconstruction, these 3D models can be edited through an intuitive interface, enabling users to apply different textures and make other modifications. This project has significant applications in various domains such as home decoration, vehicle customization, and beyond. For the 3D reconstruction in this project, we employed Nvidia's latest ngp-ins

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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 (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 o

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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 (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 w

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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 (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 (3

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Mouton, Isabelle, Shyam Katnagallu, Surendra Kumar Makineni, et al. "Calibration of Atom Probe Tomography Reconstructions Through Correlation with Electron Micrographs." Microscopy and Microanalysis 25, no. 2 (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

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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 (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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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 (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:

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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 (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 curv

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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 (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

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Lin, Ruiquan, Yunwei Ji, Wanting Ding, Tianxiang Wu, Yaosheng Zhu, and Mengxi Jiang. "A Survey on Deep Learning in 3D CAD Reconstruction." Applied Sciences 15, no. 12 (2025): 6681. https://doi.org/10.3390/app15126681.

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Three-dimensional CAD reconstruction is a long-standing and important task in fields such as industrial manufacturing, architecture, medicine, film and television, research, and education. Reconstructing CAD models remains a persistent challenge in machine learning. There have been many studies on deep learning in the field of 3D reconstruction. In recent years, with the release of CAD datasets, there have been more and more studies on 3D CAD reconstruction using deep learning. With the continuous deepening of research, deep learning has significantly improved the performance of tasks in the f

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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 effici

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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 (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

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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 3

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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 perpendicu

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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 (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 dete

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