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Journal articles on the topic '2D Images - 3D Models'

Author: Grafiati

Published: 13 July 2024

Last updated: 31 July 2025

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1

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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Yang, Guangjie, Aidi Gong, Pei Nie, et al. "Contrast-Enhanced CT Texture Analysis for Distinguishing Fat-Poor Renal Angiomyolipoma From Chromophobe Renal Cell Carcinoma." Molecular Imaging 18 (January 1, 2019): 153601211988316. http://dx.doi.org/10.1177/1536012119883161.

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Objective: To evaluate the value of 2-dimensional (2D) and 3-dimensional (3D) computed tomography texture analysis (CTTA) models in distinguishing fat-poor angiomyolipoma (fpAML) from chromophobe renal cell carcinoma (chRCC). Methods: We retrospectively enrolled 32 fpAMLs and 24 chRCCs. Texture features were extracted from 2D and 3D regions of interest in triphasic CT images. The 2D and 3D CTTA models were constructed with the least absolute shrinkage and selection operator algorithm and texture scores were calculated. The diagnostic performance of the 2D and 3D CTTA models was evaluated with

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Iyoho, Anthony E., Jonathan M. Young, Vladislav Volman, David A. Shelley, Laurel J. Ng, and Henry Wang. "3D Tibia Reconstruction Using 2D Computed Tomography Images." Military Medicine 184, Supplement_1 (2019): 621–26. http://dx.doi.org/10.1093/milmed/usy379.

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Abstract OBJECTIVE Skeletal stress fracture of the lower limbs remains a significant problem for the military. The objective of this study was to develop a subject-specific 3D reconstruction of the tibia using only a few CT images for the prediction of peak stresses and locations. METHODS Full bilateral tibial CT scans were recorded for 63 healthy college male participants. A 3D finite element (FE) model of the tibia for each subject was generated from standard CT cross-section data (i.e., 4%, 14%, 38%, and 66% of the tibial length) via a transformation matrix. The final reconstructed FE model

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García-Márquez, Marco A., Josue R. Martínez-Mireles, Jazmín Rodríguez-Flores, Arturo Austria-Cornejo, and Jorge A. Ruiz-Vanoye. "2D23D: 3D images from 2D images using statistical filters and color shading." International Journal of Combinatorial Optimization Problems and Informatics 15, no. 3 (2024): 115–25. http://dx.doi.org/10.61467/2007.1558.2024.v15i3.521.

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Below is the development of an algorithm that allows generating 3D models from 2D images for analysis and visualization, with an application focused on images from the health and space sectors, domains chosen for their contrast characteristics. The characteristics used in the project are presented and determined, as well as 3D programming and visualization environment tools that allow the generation of voxel depth, as well as the management of image formats from the medical or spatial sector, including the results obtained with images. from both sectors, from which the 3D models were created.

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Petre, Raluca-Diana, and Titus Zaharia. "3D Model-Based Semantic Categorization of Still Image 2D Objects." International Journal of Multimedia Data Engineering and Management 2, no. 4 (2011): 19–37. http://dx.doi.org/10.4018/jmdem.2011100102.

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Automatic classification and interpretation of objects present in 2D images is a key issue for various computer vision applications. In particular, when considering image/video, indexing, and retrieval applications, automatically labeling in a semantically pertinent manner/huge multimedia databases still remains a challenge. This paper examines the issue of still image object categorization. The objective is to associate semantic labels to the 2D objects present in natural images. The principle of the proposed approach consists of exploiting categorized 3D model repositories to identify unknow

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Avesta, Arman, Sajid Hossain, MingDe Lin, Mariam Aboian, Harlan M. Krumholz, and Sanjay Aneja. "Comparing 3D, 2.5D, and 2D Approaches to Brain Image Auto-Segmentation." Bioengineering 10, no. 2 (2023): 181. http://dx.doi.org/10.3390/bioengineering10020181.

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Deep-learning methods for auto-segmenting brain images either segment one slice of the image (2D), five consecutive slices of the image (2.5D), or an entire volume of the image (3D). Whether one approach is superior for auto-segmenting brain images is not known. We compared these three approaches (3D, 2.5D, and 2D) across three auto-segmentation models (capsule networks, UNets, and nnUNets) to segment brain structures. We used 3430 brain MRIs, acquired in a multi-institutional study, to train and test our models. We used the following performance metrics: segmentation accuracy, performance wit

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Osadchy, Margarita, David Jacobs, Ravi Ramamoorthi, and David Tucker. "Using specularities in comparing 3D models and 2D images." Computer Vision and Image Understanding 111, no. 3 (2008): 275–94. http://dx.doi.org/10.1016/j.cviu.2007.12.004.

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Li, Yu, Shaohua Li, and Bo Zhang. "Constructing of 3D Fluvial Reservoir Model Based on 2D Training Images." Applied Sciences 13, no. 13 (2023): 7497. http://dx.doi.org/10.3390/app13137497.

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Training images are important input parameters for multipoint geostatistical modeling, and training images that can portray 3D spatial correlations are required to construct 3D models. The 3D training images are usually obtained by unconditional simulation using algorithms such as object-based algorithms, and in some cases, it is difficult to obtain the 3D training images directly, so a series of modeling methods based on 2D training images for constructing 3D models has been formed. In this paper, a new modeling method is proposed by synthesizing the advantages of the previous methods. Taking

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Konkov, V. V., and A. B. Zamchalov. "Application of PyTorch3D and NERF Computer Vision Tools for Building a Point Cloud of a Three-Dimensional Model and Determining the Camera Position of Still Images in Space." Scientific Visualization 17, no. 1 (2025): 65–85. https://doi.org/10.26583/sv.17.1.06.

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Recently, computer graphics plays a key role in solving computer vision problems. The problem of converting 2D images into 3D models continues to be urgent, as it requires precise determination of camera position and construction of accurate 3D models of objects. Traditional methods are often limited in application and do not offer a comprehensive solution. This study examines the use of PyTorch3D and NERF libraries to determine the camera position in 3D space and create a 3D model of an object from a single 2D image. As a method of data preparation, a hardware and software system was used, in

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Choi, Chang-Hyuk, Hee-Chan Kim, Daewon Kang, and Jun-Young Kim. "Comparative study of glenoid version and inclination using two-dimensional images from computed tomography and three-dimensional reconstructed bone models." Clinics in Shoulder and Elbow 23, no. 3 (2020): 119–24. http://dx.doi.org/10.5397/cise.2020.00220.

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Background: This study was performed to compare glenoid version and inclination measured using two-dimensional (2D) images from computed tomography (CT) scans or three-dimensional (3D) reconstructed bone models.Methods: Thirty patients who had undergone conventional CT scans were included. Two orthopedic surgeons measured glenoid version and inclination three times on 2D images from CT scans (2D measurement), and two other orthopedic surgeons performed the same measurements using 3D reconstructed bone models (3D measurement). The 3D-reconstructed bone models were acquired and measured with Mim

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Zhong, Chunyan, Yanli Guo, Haiyun Huang, Liwen Tan, Yi Wu, and Wenting Wang. "Three-Dimensional Reconstruction of Coronary Arteries and Its Application in Localization of Coronary Artery Segments Corresponding to Myocardial Segments Identified by Transthoracic Echocardiography." Computational and Mathematical Methods in Medicine 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/783939.

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Objectives.To establish 3D models of coronary arteries (CA) and study their application in localization of CA segments identified by Transthoracic Echocardiography (TTE).Methods.Sectional images of the heart collected from the first CVH dataset and contrast CT data were used to establish 3D models of the CA. Virtual dissection was performed on the 3D models to simulate the conventional sections of TTE. Then, we used 2D ultrasound, speckle tracking imaging (STI), and 2D ultrasound plus 3D CA models to diagnose 170 patients and compare the results to coronary angiography (CAG).Results.3D models

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Falah .K, Rasha, and Rafeef Mohammed .H. "Convert 2D shapes in to 3D images." Journal of Al-Qadisiyah for computer science and mathematics 9, no. 2 (2017): 19–23. http://dx.doi.org/10.29304/jqcm.2017.9.2.146.

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There are several complex programs that using for convert 2D images to 3D models with difficult techniques. In this paper ,it will be introduce a useful technique and using simple Possibilities and language for converting 2D to 3D images. The technique would be used; a three-dimensional projection using three images for the same shape and display three dimensional image from different side and to implement the particular work, visual programming with 3Dtruevision engine would be used, where its given acceptable result with shorting time. And it could be used in the field of engineering drawing

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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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Sun, Lei, Xuesong Suo, Yifan Liu, Meng Zhang, and Lijuan Han. "3D Modeling of Transformer Substation Based on Mapping and 2D Images." Mathematical Problems in Engineering 2016 (2016): 1–6. http://dx.doi.org/10.1155/2016/9320502.

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A new method for building 3D models of transformer substation based on mapping and 2D images is proposed in this paper. This method segments objects of equipment in 2D images by usingk-means algorithm in determining the cluster centers dynamically to segment different shapes and then extracts feature parameters from the divided objects by using FFT and retrieves the similar objects from 3D databases and then builds 3D models by computing the mapping data. The method proposed in this paper can avoid the complex data collection and big workload by using 3D laser scanner. The example analysis sho

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Sezer, Sümeyye, Vitoria Piai, Roy P. C. Kessels, and Mark ter Laan. "Information Recall in Pre-Operative Consultation for Glioma Surgery Using Actual Size Three-Dimensional Models." Journal of Clinical Medicine 9, no. 11 (2020): 3660. http://dx.doi.org/10.3390/jcm9113660.

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Three-dimensional (3D) technologies are being used for patient education. For glioma, a personalized 3D model can show the patient specific tumor and eloquent areas. We aim to compare the amount of information that is understood and can be recalled after a pre-operative consult using a 3D model (physically printed or in Augmented Reality (AR)) versus two-dimensional (2D) MR images. In this explorative study, healthy individuals were eligible to participate. Sixty-one participants were enrolled and assigned to either the 2D (MRI/fMRI), 3D (physical 3D model) or AR groups. After undergoing a moc

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Ch. Prathima. "Construction of 3D Human Model from 2D Image using PyTorch and Blender." Communications on Applied Nonlinear Analysis 31, no. 6s (2024): 477–88. http://dx.doi.org/10.52783/cana.v31.1238.

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In this study, we present our innovative approach to generating 3D models from 2D images, employing PyTorch, Blender, Python, and OpenCV. Our method integrates image pre-processing techniques, a pre-trained model, and rendering capabilities to produce high-fidelity 3D representations. Initially, the input images, stripped of backgrounds using OpenCV, undergo pre-processing steps to enhance features relevant for 3D reconstruction, such as edge detection and depth estimation. We utilize PyTorch, a deep learning framework, to implement a leading edge model trained on large-scale 3D datasets, enab

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Bich Nhuong, Quach Thi, Pham Dinh Sac, Nguyen Minh Nhut, and Hien Thanh Le. "3D Model Reconstruction Using Gan and 2.5D Sketches from 2D Image." Jurnal Teknologi Informasi dan Pendidikan 15, no. 2 (2022): 1–11. http://dx.doi.org/10.24036/jtip.v15i2.613.

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In the current 4.0 era, many fields such as medicine, cinema, architecture, etc. often use 3D models to visualize objects. However, there is not always enough information or equipment to build a 3D model. Another approach is to take multiple 2D images and convert to 3D shapes. This method requires information on images taken of objects at different angles. To get around this, we use a 2.5D sketch as an intermediary when going from 2D to 3D. A 2D photo is easier to create a 2.5D sketch than to convert directly to a 3D shape. In this paper, we propose a model consisting of three modules: The fir

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Makarchuk, L. O., and T. A. Likhouzova. "ANALYSIS OF TOOLS FOR CREATING GRAPHIC IMAGES BASED ON 2D AND 3D MODELS." System technologies 4, no. 159 (2025): 166–71. https://doi.org/10.34185/1562-9945-4-159-2025-17.

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The study analyzes and defines the current state of technologies in creating graphic images and outlines the main problems that need to be solved. Key aspects of working with 2D and 3D graphics are highlighted, and the features of their combination are determined. Modern IT solutions in this area are analyzed, which makes it possible to assess the available technologies and their limitations. In particular, it was found that most existing software products are focused mainly on 2D graphics, while the integration of 3D elements remains insufficiently implemented or difficult for non-programmer

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Greuter, Ladina, Adriana De Rosa, Philippe Cattin, Davide Marco Croci, Jehuda Soleman, and Raphael Guzman. "Randomized study comparing 3D virtual reality and conventional 2D on-screen teaching of cerebrovascular anatomy." Neurosurgical Focus 51, no. 2 (2021): E18. http://dx.doi.org/10.3171/2021.5.focus21212.

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OBJECTIVE Performing aneurysmal clipping requires years of training to successfully understand the 3D neurovascular anatomy. This training has traditionally been obtained by learning through observation. Currently, with fewer operative aneurysm clippings, stricter work-hour regulations, and increased patient safety concerns, novel teaching methods are required for young neurosurgeons. Virtual-reality (VR) models offer the opportunity to either train a specific surgical skill or prepare for an individual surgery. With this study, the authors aimed to compare the spatial orientation between trad

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Filaliansary, Tank, Jean-Philippe Vandeborre, and Mohamed Daoudi. "A framework for 3D CAD models retrieval from 2D images." Annales Des Télécommunications 60, no. 11-12 (2005): 1337–59. http://dx.doi.org/10.1007/bf03219852.

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Franken, Thomas, Matteo Dellepiane, Fabio Ganovelli, Paolo Cignoni, Claudio Montani, and Roberto Scopigno. "Minimizing user intervention in registering 2D images to 3D models." Visual Computer 21, no. 8-10 (2005): 619–28. http://dx.doi.org/10.1007/s00371-005-0309-z.

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Marcillat, Marin, Loic Van Audenhaege, Catherine Borremans, Aurélien Arnaubec, and Lenaick Menot. "The best of two worlds: reprojecting 2D image annotations onto 3D models." PeerJ 12 (June 28, 2024): e17557. http://dx.doi.org/10.7717/peerj.17557.

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Imagery has become one of the main data sources for investigating seascape spatial patterns. This is particularly true in deep-sea environments, which are only accessible with underwater vehicles. On the one hand, using collaborative web-based tools and machine learning algorithms, biological and geological features can now be massively annotated on 2D images with the support of experts. On the other hand, geomorphometrics such as slope or rugosity derived from 3D models built with structure from motion (sfm) methodology can then be used to answer spatial distribution questions. However, preci

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Guček Puhar, Enej, Lidija Korat, Miran Erič, Aleš Jaklič, and Franc Solina. "Microtomographic Analysis of a Palaeolithic Wooden Point from the Ljubljanica River." Sensors 22, no. 6 (2022): 2369. http://dx.doi.org/10.3390/s22062369.

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A rare and valuable Palaeolithic wooden point, presumably belonging to a hunting weapon, was found in the Ljubljanica River in Slovenia in 2008. In order to prevent complete decay, the waterlogged wooden artefact had to undergo conservation treatment, which usually involves some expected deformations of structure and shape. To investigate these changes, a series of surface-based 3D models of the artefact were created before, during and after the conservation process. Unfortunately, the surface-based 3D models were not sufficient to understand the internal processes inside the wooden artefact (

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Tang, Zhenyu, Junwu Zhang, Xinhua Cheng, et al. "Cycle3D: High-quality and Consistent Image-to-3D Generation via Generation-Reconstruction Cycle." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 7 (2025): 7320–28. https://doi.org/10.1609/aaai.v39i7.32787.

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Recent 3D large reconstruction models typically employ a two-stage process, including first generate multi-view images by a multi-view diffusion model, and then utilize a feed-forward model to reconstruct images to 3D content. However, multi-view diffusion models often produce low-quality and inconsistent images, adversely affecting the quality of the final 3D reconstruction. To address this issue, we propose a unified 3D generation framework called Cycle3D, which cyclically utilizes a 2D diffusion-based generation module and a feed-forward 3D reconstruction module during the multi-step diffus

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Hayase, Mitsuhiro, and Susumu Shimada. "Posture Estimation of Human Body Based on Connection Relations of 3D Ellipsoidal Models." Journal of Advanced Computational Intelligence and Intelligent Informatics 14, no. 6 (2010): 638–44. http://dx.doi.org/10.20965/jaciii.2010.p0638.

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We propose new method of estimating human body posture from connection relations of threedimensional (3D) ellipsoidal models. First, 3D ellipsoidal models with enlargement and reduction transformations are constructed. Next, two-dimensional (2D) appearance models are constructed from 2D projected images of the 3D model. The appearance models are related to each other by employing a network data structure. They are then matched with an image of the body made from actual thermal images. By using the connection relations between the head and body, the head and the body can be recognized. Differen

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Ozturk, Huseyin Yasin, and Emanuele Zappa. "Automated Crack Width Measurement in 3D Models: A Photogrammetric Approach with Image Selection." Information 16, no. 6 (2025): 448. https://doi.org/10.3390/info16060448.

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Structural cracks can critically undermine infrastructure integrity, driving the need for precise, scalable inspection methods beyond conventional visual or 2D image-based approaches. This study presents an automated system integrating photogrammetric 3D reconstruction with deep learning to quantify crack dimensions in a spatial context. Multiple images are processed via Agisoft Metashape to generate high-fidelity 3D meshes. Then, a subset of images are automatically selected based on camera orientation and distance, and a deep learning algorithm is applied to detect cracks in 2D images. The d

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Anderson, Timothy I., Kelly M. Guan, Bolivia Vega, Saman A. Aryana, and Anthony R. Kovscek. "RockFlow: Fast Generation of Synthetic Source Rock Images Using Generative Flow Models." Energies 13, no. 24 (2020): 6571. http://dx.doi.org/10.3390/en13246571.

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Image-based evaluation methods are a valuable tool for source rock characterization. The time and resources needed to obtain images has spurred development of machine-learning generative models to create synthetic images of pore structure and rock fabric from limited image data. While generative models have shown success, existing methods for generating 3D volumes from 2D training images are restricted to binary images and grayscale volume generation requires 3D training data. Shale characterization relies on 2D imaging techniques such as scanning electron microscopy (SEM), and grayscale value

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Upadhye, Gopal D., Anant Kaulage, Ranjeetsingh S. Suryawanshi, et al. "A Survey of Algorithms Involved in the Conversion of 2-D Images to 3-D Model." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 6s (2023): 358–70. http://dx.doi.org/10.17762/ijritcc.v11i6s.6942.

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Since the advent of machine learning, deep neural networks, and computer graphics, the field of 2D image to 3D model conversion has made tremendous strides. As a result, many algorithms and methods for converting 2D to 3D images have been developed, including SFM, SFS, MVS, and PIFu. Several strategies have been compared, and it was found that each has pros and cons that make it appropriate for particular applications. For instance, SFM is useful for creating realistic 3D models from a collection of pictures, whereas SFS is best for doing so from a single image. While PIFu can create extremely

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ALEKSANDROVA, O. "3D FACE MODEL RECONSTRUCTING FROM ITS 2D IMAGES USING NEURAL NETWORKS." Scientific papers of Donetsk National Technical University. Series: Informatics, Cybernetics and Computer Science 2 - №1, no. 33-34 (2022): 57–64. http://dx.doi.org/10.31474/1996-1588-2021-2-33-57-64.

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The most common methods of reconstruction of 3D-models of the face are considered, their quantitative estimates are analyzed and determined, the most promising approach is highlighted - 3D Morphable Model. The necessity of its modification in order to improve the results of reconstruction based on the analysis of the main components and the use of generative-competitive neural network is substantiated. One of the advantages of using the 3D Morphable Model with principal component analysis is to present only a plausible solution when the solution space is limited, which simplifies the problem t

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Cao, Ping, Jie Gao, and Zuping Zhang. "Multi-View Based Multi-Model Learning for MCI Diagnosis." Brain Sciences 10, no. 3 (2020): 181. http://dx.doi.org/10.3390/brainsci10030181.

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Mild cognitive impairment (MCI) is the early stage of Alzheimer’s disease (AD). Automatic diagnosis of MCI by magnetic resonance imaging (MRI) images has been the focus of research in recent years. Furthermore, deep learning models based on 2D view and 3D view have been widely used in the diagnosis of MCI. The deep learning architecture can capture anatomical changes in the brain from MRI scans to extract the underlying features of brain disease. In this paper, we propose a multi-view based multi-model (MVMM) learning framework, which effectively combines the local information of 2D images wit

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Buccino, Federica, Chiara Colombo, Daniel Hernando Lozano Duarte, Luca Rinaudo, Fabio Massimo Ulivieri, and Laura Maria Vergani. "2D and 3D numerical models to evaluate trabecular bone damage." Medical & Biological Engineering & Computing 59, no. 10 (2021): 2139–52. http://dx.doi.org/10.1007/s11517-021-02422-x.

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AbstractThe comprehension of trabecular bone damage processes could be a crucial hint for understanding how bone damage starts and propagates. Currently, different approaches to bone damage identification could be followed. Clinical approaches start from dual X-ray absorptiometry (DXA) technique that can evaluate bone mineral density (BMD), an indirect indicator of fracture risk. DXA is, in fact, a two-dimensional technology, and BMD alone is not able to predict the effective risk of fractures. First attempts in overcoming this issue have been performed with finite element (FE) methods, combin

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JIA, JIN, and KEIICHI ABE. "RECOGNIZING 3D OBJECTS BY USING MODELS LEARNED AUTOMATICALLY FROM 2D TRAINING IMAGES." International Journal of Pattern Recognition and Artificial Intelligence 14, no. 03 (2000): 315–38. http://dx.doi.org/10.1142/s0218001400000210.

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A scheme for learning and recognizing 3D objects from their 2D views is presented. The scheme proceeds in two stages. In the first stage, we try to learn a prototype automatically from 2D training images of different objects which belong to the same object class and consequently have similar shapes of parts and similar adjacency relations between the parts. In the second stage, the generated prototype is used to recognize learned objects or the objects similar to the learned ones from images of complex real scenes. We tested the approach on recognizing some simple objects from images of indoor

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Park, Sungsoo, and Hyeoncheol Kim. "3DPlanNet: Generating 3D Models from 2D Floor Plan Images Using Ensemble Methods." Electronics 10, no. 22 (2021): 2729. http://dx.doi.org/10.3390/electronics10222729.

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Research on converting 2D raster drawings into 3D vector data has a long history in the field of pattern recognition. Prior to the achievement of machine learning, existing studies were based on heuristics and rules. In recent years, there have been several studies employing deep learning, but a great effort was required to secure a large amount of data for learning. In this study, to overcome these limitations, we used 3DPlanNet Ensemble methods incorporating rule-based heuristic methods to learn with only a small amount of data (30 floor plan images). Experimentally, this method produced a w

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Chrysochoou, Dimosthenis, Ariana Familiar, Deep Gandhi, et al. "IMG-08. SYNTHESIZING MISSING MRI SEQUENCES IN PEDIATRIC BRAIN TUMORS USING GENERATIVE ADVERSARIAL NETWORKS; TOWARDS IMPROVED VOLUMETRIC TUMOR ASSESSMENT." Neuro-Oncology 26, Supplement_4 (2024): 0. http://dx.doi.org/10.1093/neuonc/noae064.345.

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Abstract BACKGROUND Standard MRI sequences, such as pre- and post-contrast T1w, T2w, and FLAIR images, are essential for optimizing segmentation of tumor subregions and evaluating treatment responses in pediatric brain tumors (PBTs). However, MRI sets are often incomplete due to imaging artifacts or inconsistent acquisition protocols across various centers. Generative Adversarial Networks (GANs) have been effectively utilized to generate missing MRI sequences for adult brain tumors. This study applies image-to-image translation models using GANs to synthesize missing FLAIR images from T2w imag

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Iwashita, Yumi, Ryo Kurazume, Kozo Konishi, Masahiko Nakamoto, Makoto Hashizume, and Tsutomu Hasegawa. "Fast alignment of 3D geometrical models and 2D grayscale images using 2D distance maps." Systems and Computers in Japan 38, no. 14 (2007): 52–62. http://dx.doi.org/10.1002/scj.20634.

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Bobulski, J. "Multimodal face recognition method with two-dimensional hidden Markov model." Bulletin of the Polish Academy of Sciences Technical Sciences 65, no. 1 (2017): 121–28. http://dx.doi.org/10.1515/bpasts-2017-0015.

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Abstract The paper presents a new solution for the face recognition based on two-dimensional hidden Markov models. The traditional HMM uses one-dimensional data vectors, which is a drawback in the case of 2D and 3D image processing, because part of the information is lost during the conversion to one-dimensional features vector. The paper presents a concept of the full ergodic 2DHMM, which can be used in 2D and 3D face recognition. The experimental results demonstrate that the system based on two dimensional hidden Markov models is able to achieve a good recognition rate for 2D, 3D and multimo

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Wang, Feng, Weichuan Ni, Shaojiang Liu, Zhiming Xu, Zemin Qiu, and Zhiping Wan. "A 2D image 3D reconstruction function adaptive denoising algorithm." PeerJ Computer Science 9 (October 3, 2023): e1604. http://dx.doi.org/10.7717/peerj-cs.1604.

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To address the issue of image denoising algorithms blurring image details during the denoising process, we propose an adaptive denoising algorithm for the 3D reconstruction of 2D images. This algorithm takes into account the inherent visual characteristics of human eyes and divides the image into regions based on the entropy value of each region. The background region is subject to threshold denoising, while the target region undergoes processing using an adversarial generative network. This network effectively handles 2D target images with noise and generates a 3D model of the target. The pro

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Prabha, Navaneeth, Naeema Ziyad, Navya Prasad, Jisha P. Abraham, Pristy Paul T, and Rini T Paul. "Enhanced Medical Analysis: Leveraging 3D Visualization and VR-AR Technology." Journal of Sensor Networks and Data Communications 4, no. 3 (2024): 01–09. https://doi.org/10.33140/jsndc.04.03.03.

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Modern healthcare depends heavily on medical imaging, but traditional 2D images frequently lack depth and detail. This paper introduces a novel approach, that turns 2D medical images, such as X-rays, MRIs, and CT scans, into immersive three-dimensional visualizations using virtual and augmented reality (VR/AR) technology. The process consists of four steps: acquiring DICOM medical data, converting the data into 3D models, applying the rendering modes and slicing planes, and deploying the data in VR/AR environments. Preprocessing methods evaluate and improve the quality of medical image data, w

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Artamonova, N. B., S. V. Sheshenin, E. A. Orlov, Zhou Bichen, J. V. Frolova, and I. R. Khamidullin. "Calculation of effective properties of geocomposites based on computed tomography images." PNRPU Mechanics Bulletin, no. 3 (December 15, 2022): 83–94. http://dx.doi.org/10.15593/perm.mech/2022.3.09.

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Biot’s parameter is included in the formula for calculating effective stresses and should be taken into account when assessing the stress-strain state of a water-saturated rock mass. A method for calculating Biot’s tensor parameter based on asymptotic averaging of the equilibrium equation for a fluid-saturated porous medium is proposed. Calculations of elastic properties and Biot’s coefficient were carried out on various types of rocks - limestone, dolomite, hyaloclastite, basalt. The calculations were carried out using 3D models of geocomposites built from X-ray computed tomography images. Th

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Cashman, Thomas J., and Andrew W. Fitzgibbon. "What Shape Are Dolphins? Building 3D Morphable Models from 2D Images." IEEE Transactions on Pattern Analysis and Machine Intelligence 35, no. 1 (2013): 232–44. http://dx.doi.org/10.1109/tpami.2012.68.

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Clemens, D. T., and D. W. Jacobs. "Space and time bounds on indexing 3D models from 2D images." IEEE Transactions on Pattern Analysis and Machine Intelligence 13, no. 10 (1991): 1007–17. http://dx.doi.org/10.1109/34.99235.

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Nicholson, Kristen F., R. Tyler Richardson, Freeman Miller, and James G. Richards. "Determining 3D scapular orientation with scapula models and biplane 2D images." Medical Engineering & Physics 41 (March 2017): 103–8. http://dx.doi.org/10.1016/j.medengphy.2017.01.012.

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Al Khalil, O., and P. Grussenmeyer. "2D & 3D RECONSTRUCTION WORKFLOWS FROM ARCHIVE IMAGES, CASE STUDY OF DAMAGED MONUMENTS IN BOSRA AL-SHAM CITY (SYRIA)." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W15 (August 19, 2019): 55–62. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w15-55-2019.

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<p><strong>Abstract.</strong> The paper explores the possibilities of using old images for 2D and 3D documentation of archaeological monuments using open source, free and commercial photogrammetric software. The available images represent the external façade of the Western gate and Al Omari Mosque in the city of Bosra al-Sham in Syria, which were severely damaged during the recent war. The images were captured using consumer camera and they were originally used to achieve 2D documentation for each part of the gate separately. 2D control points were used to scale the digital p

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Li, Yinhai, Fei Wang, and Xinhua Hu. "Deep-Learning-Based 3D Reconstruction: A Review and Applications." Applied Bionics and Biomechanics 2022 (September 15, 2022): 1–6. http://dx.doi.org/10.1155/2022/3458717.

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In recent years, deep learning models have been widely used in 3D reconstruction fields and have made remarkable progress. How to stimulate deep academic interest to effectively manage the explosive augmentation of 3D models has been a research hotspot. This work shows mainstream 3D model retrieval algorithm programs based on deep learning currently developed remotely, and further subdivides their advantages and disadvantages according to the behavior evaluation of the algorithm programs obtained by trial. According to other restoration applications, the main 3D model retrieval algorithms can

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Sinchuk, Yuriy, Stefan Dietrich, Matthias Merzkirch, Kay André Weidenmann, and Romana Piat. "Micro-Computed Tomography Image Based Numerical Elastic Homogenization of MMCs." Key Engineering Materials 627 (September 2014): 437–40. http://dx.doi.org/10.4028/www.scientific.net/kem.627.437.

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Properties of an interpenetrating metal–ceramic composite with freeze-cast preforms are investigated. For the estimation of elastic properties of the composite numerical homogenization approaches for 2D and 3D finite element models are implemented. The FE models are created based on micro-computed tomography (μCT) images. The results of the numerical 2D and 3D modeling coincide and are in good agreement with available experimental measurements of elastic properties.

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Wang, W. X., G. X. Zhong, J. J. Huang, X. M. Li, and L. F. Xie. "INSTANCE SEGMENTATION OF 3D MESH MODEL BY INTEGRATING 2D AND 3D DATA." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W2-2023 (December 14, 2023): 1677–84. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w2-2023-1677-2023.

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Abstract. Buildings are an important part of the urban scene. In this paper, a novel instance segmentation framework for 3D mesh models in urban scenes is proposed. Unlike existing works focusing on semantic segmentation of urban scenes, this work focuses on detecting and segmenting 3D building instances even if they are attached and occluded in a large and imprecise 3D surface model. Multi-view images are first enhanced to RGBH images by adding a height map and are segmented to obtain all roof instances using Mask R-CNN. The 2D roof instances are then back-projected onto the 3D scene, the acc

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Barazzetti, L., M. Previtali, and W. Rose. "FLATTENING COMPLEX ARCHITECTURAL SURFACES: PHOTOGRAMMETRIC 3D MODELS CONVERTED INTO 2D MAPS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2/W4-2024 (February 14, 2024): 33–40. http://dx.doi.org/10.5194/isprs-archives-xlviii-2-w4-2024-33-2024.

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Abstract. The paper describes a workflow to flatten 3D photogrammetric models of undevelopable surfaces into unfragmented 2D texture maps. The aim is to create a texture map with reduced fragmentation compared to typical photogrammetric texture files associated with 3D models. Geometric reformatting of the mesh is required to achieve an unfragmented final texture image with enough visual quality to allow for its use in 2D editing software (e.g., Photoshop, Illustrator, etc.). With a lighter model and defragmented texture, graphic documentation of conditions, treatments, or other relevant infor

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Badr, Ahmed Mangoud, Wael M. Mubarak Refai, Mohamed Gaber El-Shal, and Ahmed Nasef Abdelhameed. "Accuracy and Reliability of Kinect Motion Sensing Input Device’s 3D Models: A Comparison to Direct Anthropometry and 2D Photogrammetry." Open Access Macedonian Journal of Medical Sciences 9, no. D (2021): 54–60. http://dx.doi.org/10.3889/oamjms.2021.6006.

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AIM: This study aims to evaluate the accuracy and reliability of Kinect motion sensing input device’s three-dimensional (3D) models by comparing it with direct anthropometry and digital 2D photogrammetry. MATERIALS AND METHODS: Six profiles and four frontal parameters were directly measured on the faces of 80 participants. The same measurements were repeated using two-dimensional (2D) photogrammetry and (3D) images obtained from Kinect device. Another observer made the same measurements for 30% of the images obtained with 3D technique, and interobserver reproducibility was evaluated for 3D ima

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Anay, Dongre. "Neural Radiance Fields-Comprehensive Survey." International Journal of Innovative Science and Research Technology 8, no. 1 (2023): 968–72. https://doi.org/10.5281/zenodo.7597137.

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Neural Radiance Fields (NeRF) is a machine learning model that can generate high-resolution, photorealistic 3D models of scenes or objects from a set of 2D images. It does this by learning a continuous 3D function that maps positions in 3D space to the radiance (intensity and color) of the light that would be observed at that position in the scene. To create a NeRF model, the model is trained on a dataset of 2D images of the scene or object, along with their corresponding 3D positions and orientations. The model learns to predict the radiance at each 3D position in the scene by using a combina

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Mehranfar, M., H. Arefi, and F. Alidoost. "A PROJECTION-BASED RECONSTRUCTION ALGORITHM FOR 3D MODELING OF BRIDGE STRUCTURES FROM DRONE-BASED POINT CLOUD." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-4/W1-2021 (September 3, 2021): 77–83. http://dx.doi.org/10.5194/isprs-archives-xlvi-4-w1-2021-77-2021.

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Abstract. This paper presents a projection-based method for 3D bridge modeling using dense point clouds generated from drone-based images. The proposed workflow consists of hierarchical steps including point cloud segmentation, modeling of individual elements, and merging of individual models to generate the final 3D model. First, a fuzzy clustering algorithm including the height values and geometrical-spectral features is employed to segment the input point cloud into the main bridge elements. In the next step, a 2D projection-based reconstruction technique is developed to generate a 2D model

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