Bibliografías temáticas / 3D Computer Vision

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Literatura académica sobre el tema "3D Computer Vision"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 26 de julio de 2025

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Artículos de revistas sobre el tema "3D Computer Vision"

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Hanry, Ham, Wesley Julian, and Hendra. "Computer vision based 3D reconstruction : A review." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 4 (2019): 2394–402. https://doi.org/10.11591/ijece.v9i4.pp2394-2402.

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3D reconstruction are used in many fields starts from the object reconstruction such as site, cultural artifacts in both ground and under the sea levels, medical imaging data, nuclear substantional. The scientist are beneficial for these task in order to learn, keep and better visual enhancement into 3D data. In this paper we differentiate the algorithm used depends on the input image: single still image, RGB-Depth image, multiperspective of 2D images, and video sequences. The prior works also explained how the 3D reconstruction perform in many fields and using various algorithms.
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Ohta, Yuichi. "3D Image Media and Computer Vision -From CV as Robot Technology to CV as Media Technology-." Journal of Robotics and Mechatronics 9, no. 2 (1997): 92–97. http://dx.doi.org/10.20965/jrm.1997.p0092.

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The possibility to apply the computer vision technology to the development of a new image medium is discussed. Computer vision has been studied as a sensor technology between the real world and computers. On the other hand, the computer graphics are the interface technology between the computers and human beings. The invention of ""3D photography"" based on the computer vision technology will realize a new 3D image medium which connects the real world and the human beings via computer. In such a framework, computer vision should be studied as a media technology rather than a robot technology.
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Šára, Radim. "What can Computer Graphics expect from 3D Computer Vision?" Computer Graphics Forum 26, no. 3 (2007): xix. http://dx.doi.org/10.1111/j.1467-8659.2007.01045.x.

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Ham, Hanry, Julian Wesley, and Hendra Hendra. "Computer Vision Based 3D Reconstruction : A Review." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 4 (2019): 2394. http://dx.doi.org/10.11591/ijece.v9i4.pp2394-2402.

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3D reconstruction are used in many fields starts from the object reconstruction such as site, and cultural artifacts in both ground and under the sea levels. The scientist are beneficial for these task in order to learn and keep the environment into 3D data due to the extinction. In this paper explained vision setup that is commonly used such as single camera, stereo camera, Kinect / Structured Light/ Time of Flight camera and fusion approach. The prior works also explained how the 3D reconstruction perform in many fields and using various algorithms.
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Yi, Zhenghui. "Application of Computer Vision in 3D Film." Journal of Physics: Conference Series 1915, no. 2 (2021): 022026. http://dx.doi.org/10.1088/1742-6596/1915/2/022026.

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Poggi, Matteo, and Thomas B. Moeslund. "Computer Vision for 3D Perception and Applications." Sensors 21, no. 12 (2021): 3944. http://dx.doi.org/10.3390/s21123944.

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Kabayama, A. M., and L. G. Trabasso. "Performance evaluation of 3D computer vision techniques." Journal of the Brazilian Society of Mechanical Sciences 24, no. 3 (2002): 234–38. http://dx.doi.org/10.1590/s0100-73862002000300013.

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Fjeldaas, Sven. "Computer vision supported by 3D geometric modelling." Advances in Manufacturing 2, no. 1 (2014): 22–31. http://dx.doi.org/10.1007/s40436-014-0068-z.

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Mamand, Samar Sabah, and Abdulqadir Ismail Abdulla. "2D to 3D Image Conversion Algorithms." ITM Web of Conferences 64 (2024): 01010. http://dx.doi.org/10.1051/itmconf/20246401010.

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With the emergence of Artificial Intelligence (AI), there are many applications for 3D computer vision, and different problems in diversified domains are being solved. Particularly deep learning and image processing techniques are widely used in computer vision applications, for example, medical imaging which commonly uses 2D images to see human organs can benefit tremendously from 3D reconstruction of a human organ or cancer lesions for diagnosis purposes. Cars nowadays use radar, lidar, and sensors to create an awareness of the surroundings of the car, having a 3D reconstruction of the car’s
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Icasio-Hernández, O., Y. I. Curiel-Razo, C. C. Almaraz-Cabral, S. R. Rojas-Ramirez, and J. J. González-Barbosa. "MEASUREMENT ERROR WITH DIFFERENT COMPUTER VISION TECHNIQUES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 12, 2017): 227–35. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-227-2017.

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The goal of this work is to offer a comparative of measurement error for different computer vision techniques for 3D reconstruction and allow a metrological discrimination based on our evaluation results. The present work implements four 3D reconstruction techniques: passive stereoscopy, active stereoscopy, shape from contour and fringe profilometry to find the measurement error and its uncertainty using different gauges. We measured several dimensional and geometric known standards. We compared the results for the techniques, average errors, standard deviations, and uncertainties obtaining a
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Tesis sobre el tema "3D Computer Vision"

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Mehmood, Zahid. "A 3D optical vision system." Thesis, University of Liverpool, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.284253.

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Park, Allen S. M. (Allen S. ). Massachusetts Institute of Technology. "Machine-vision assisted 3D printing." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/113162.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 71-72).<br>I augmented a 3D printer with software for a 3D scanning system in order to incorporate feedback into the printing process. After calibration of the scanning system and the printer, the 3D scanning system is capab
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Vicenti, Jasper Fourways. "Aural imaging from 3D vision." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/37085.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.<br>Includes bibliographical references (p. 55-56).<br>Dense stereo maps have many useful applications in computer vision. They can help in performing such tasks as background separation, segmentation, and object recognition. Despite the continued exponential advances in the computational capacity of microprocessors, current personal computers are limited in their ability to adequately solve the dense stereo vision problem in realtime. Modern processors now contain a SIMD instruc
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Malan, Daniel Francois. "3D tracking between satellites using monocular computer vision." Thesis, Stellenbosch : University of Stellenbosch, 2005. http://hdl.handle.net/10019.1/3081.

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Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2005.<br>Visually estimating three-dimensional position, orientation and motion, between an observer and a target, is an important problem in computer vision. Solutions which compute threedimensional movement from two-dimensional intensity images, usually rely on stereoscopic vision. Some research has also been done in systems utilising a single (monocular) camera. This thesis investigates methods for estimating position and pose from monocular image sequences. The intended future application is of visual
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Christie, Gordon A. "Computer Vision for Quarry Applications." Thesis, Virginia Tech, 2013. http://hdl.handle.net/10919/42762.

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This thesis explores the use of computer vision to facilitate three different processes of a quarry's operation. The first is the blasting process. This is where operators determine where to drill in order to execute an efficient and safe blast. Having an operator manually determine the drilling angles and positions can lead to inefficient and dangerous blasts. By using two cameras, oriented vertically, and separated by a fixed baseline, Structure from Motion techniques can be used to create a scaled 3D model of a bench. This can then be analyzed to provide operators with borehole locations an
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Chyou, Te-yu. "A 3D Computer Vision System in Radiotherapy Patient Setup." Thesis, University of Canterbury. Physics and Astronomy, 2012. http://hdl.handle.net/10092/7176.

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An approach to quantitatively determine patient surface contours as part of an augmented reality (AR) system for patient position and posture correction was developed. Quantitative evaluation of the accuracy of patient positioning and posture correction requires the knowledge of coordinates of the patient contour. The system developed uses the surface contours from the planning CT data as the reference surface coordinates. The corresponding reference point cloud is displayed on screen to enable AR assisted patient positioning. A 3D computer vision system using structured light then captures t
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Vidas, Stephen G. "Handheld 3D thermography using range sensing and computer vision." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/68030/1/Stephen_Vidas_Thesis.pdf.

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This thesis developed a method for real-time and handheld 3D temperature mapping using a combination of off-the-shelf devices and efficient computer algorithms. It contributes a new sensing and data processing framework to the science of 3D thermography, unlocking its potential for application areas such as building energy auditing and industrial monitoring. New techniques for the precise calibration of multi-sensor configurations were developed, along with several algorithms that ensure both accurate and comprehensive surface temperature estimates can be made for rich 3D models as they are
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Kaesemodel, Pontes Jhony. "Representation and reconstruction of 3D shapes in computer vision." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/174357/1/Jhony_Kaesemodel%20Pontes_Thesis.pdf.

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Given a single image taken from any popular digital camera, how can an object from the scene be reconstructed, or "grabbed", from the picture as a three-dimensional (3D) object? We humans do it effortlessly! This thesis explores how to give computers such a 3D perception. We study how to efficiently represent 3D objects to allow them to be reconstructed from an image. This is challenging as there might exist different shapes that have similar 2D projection. To constrain this ill-posed problem, we propose novel methods to encode the object's geometry and to reconstruct them by relying on 3D pri
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Breckon, Toby P. "Completing unknown portions of 3D scenes by 3D visual propagation." Thesis, University of Edinburgh, 2006. http://hdl.handle.net/1842/1244.

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As the requirement for more realistic 3D environments is pushed forward by the computer {graphics | movie | simulation | games} industry, attention turns away from the creation of purely synthetic, artist derived environments towards the use of real world captures from the 3D world in which we live. However, common 3D acquisition techniques, such as laser scanning and stereo capture, are realistically only 2.5D in nature - such that the backs and occluded portions of objects cannot be realised from a single uni-directional viewpoint. Although multi-directional capture has existed for sometime,
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Wang, Shaoxiong S. M. Massachusetts Institute of Technology. "3D shape perception from vision and touch." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122914.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 47-52).<br>Perceiving accurate 3D object shape is important for robots to interact with the physical world. Current research along this direction has been primarily relying on visual observations. Vision, however useful, has inhe
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Libros sobre el tema "3D Computer Vision"

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Wöhler, Christian. 3D Computer Vision. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4150-1.

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Wöhler, Christian. 3D Computer Vision. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01732-2.

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Zhang, Yu-Jin. 3D Computer Vision. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-19-7603-2.

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Wöhler, Christian. 3D Computer Vision: Efficient Methods and Applications. 2nd ed. Springer London, 2013.

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Zhang, Zhengyou. 3D dynamic scene analysis: A stereo basedapproach. Springer-Verlag, 1992.

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Cyganek, Boguslaw. An introduction to 3D computer vision techniques and algorithms. John Wiley & Sons, 2008.

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Cyganek, Bogusław, and J. Paul Siebert. An Introduction to 3D Computer Vision Techniques and Algorithms. John Wiley & Sons, Ltd, 2009. http://dx.doi.org/10.1002/9780470699720.

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Abdelguerfi, Mahdi. 3D Synthetic Environment Reconstruction. Springer US, 2001.

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Zhang, David. 3D Biometrics: Systems and Applications. Springer New York, 2013.

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Nicosevici, Tudor. Efficient 3D Scene Modeling and Mosaicing. Springer Berlin Heidelberg, 2013.

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Capítulos de libros sobre el tema "3D Computer Vision"

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Breiding, Paul, Kathlén Kohn, and Bernd Sturmfels. "Computer Vision." In Oberwolfach Seminars. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51462-3_13.

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AbstractThe field of computer vision studies how computers can gain understanding from images and videos, similar to human cognitive abilities. One of the classical challenges is to reconstruct a 3D object from images taken by several unknown cameras. While the resulting questions in multiview geometry [80] have a long history in computer vision, recent years have seen a confluence with ideas and algorithms from algebraic geometry.
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Zhang, Yu-Jin. "Correction to: Simultaneous Location and Mapping." In 3D Computer Vision. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-19-7603-2_12.

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Zhang, Yu-Jin. "Monocular Multi-image Scene Restoration." In 3D Computer Vision. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-19-7603-2_7.

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Zhang, Yu-Jin. "3D Point Cloud Data and Processing." In 3D Computer Vision. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-19-7603-2_4.

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Zhang, Yu-Jin. "Binocular Stereovision." In 3D Computer Vision. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-19-7603-2_5.

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Zhang, Yu-Jin. "Multi-ocular Stereovision." In 3D Computer Vision. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-19-7603-2_6.

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Zhang, Yu-Jin. "Spatial-Temporal Behavior Understanding." In 3D Computer Vision. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-19-7603-2_11.

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Zhang, Yu-Jin. "Monocular Single-Image Scene Restoration." In 3D Computer Vision. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-19-7603-2_8.

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Zhang, Yu-Jin. "Introduction." In 3D Computer Vision. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-19-7603-2_1.

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Zhang, Yu-Jin. "Depth Image Acquisition." In 3D Computer Vision. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-19-7603-2_3.

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Actas de conferencias sobre el tema "3D Computer Vision"

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Duan, Hao-Bin, Miao Wang, Yan-Xun Li, and Yong-Liang Yang. "Neural 3D Strokes: Creating Stylized 3D Scenes with Vectorized 3D Strokes." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.00501.

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Liu, Liyang, Shanshan Yu, Long Chen, et al. "Shared 3D Robotic Arm Control Based on Intracortical Brain-Computer Interface and Computer Vision." In 2024 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2024. https://doi.org/10.1109/robio64047.2024.10907485.

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Dong, Bo, Xinfang Cui, Huanxiang Ding, and Xiaoru Sun. "Application of Computer Vision Algorithms in 3D Reconstruction of Scoliosis Images." In 2025 3rd International Conference on Data Science and Information System (ICDSIS). IEEE, 2025. https://doi.org/10.1109/icdsis65355.2025.11070683.

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Aanaes, Henrik. "Data from 3D Computer Vision." In The 2nd World Congress on Electrical Engineering and Computer Systems and Science. Avestia Publishing, 2016. http://dx.doi.org/10.11159/mvml16.1.

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Guo, Yulan, Jun Zhang, Min Lu, Jianwei Wan, and Yanxin Ma. "Benchmark datasets for 3D computer vision." In 2014 IEEE 9th Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2014. http://dx.doi.org/10.1109/iciea.2014.6931468.

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Holler, Mark, Ingrid Carlbom, Steven Feiner, George Robertson, and Demetri Terzopoulos. "Computer vision in 3D interactivity (panel)." In ACM SIGGRAPH 98 Conference abstracts and applications. ACM Press, 1998. http://dx.doi.org/10.1145/280953.281598.

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Demir, Erkan, Kazim Yildiz, Onder Demir, and Eyup Emre Ulku. "Computer Vision Based Intelligent 3D CNC Machines." In 2020 Innovations in Intelligent Systems and Applications Conference (ASYU). IEEE, 2020. http://dx.doi.org/10.1109/asyu50717.2020.9259826.

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Zhang, Huanle, Bo Han, and Prasant Mohapatra. "Toward Mobile 3D Vision." In 2020 29th International Conference on Computer Communications and Networks (ICCCN). IEEE, 2020. http://dx.doi.org/10.1109/icccn49398.2020.9209700.

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Meixner, Philipp, Franz Leberl, and Mathieu Bredif. "3D roof details by 3D aerial vision." In 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops). IEEE, 2011. http://dx.doi.org/10.1109/iccvw.2011.6130245.

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KHALOO, ALI, and DAVID LATTANZI. "Integrating 3D Computer Vision and Robotic Infrastructure Inspection." In Structural Health Monitoring 2017. DEStech Publications, Inc., 2017. http://dx.doi.org/10.12783/shm2017/14231.

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Informes sobre el tema "3D Computer Vision"

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Huang, Haohang, Erol Tutumluer, Jiayi Luo, Kelin Ding, Issam Qamhia, and John Hart. 3D Image Analysis Using Deep Learning for Size and Shape Characterization of Stockpile Riprap Aggregates—Phase 2. Illinois Center for Transportation, 2022. http://dx.doi.org/10.36501/0197-9191/22-017.

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Riprap rock and aggregates are extensively used in structural, transportation, geotechnical, and hydraulic engineering applications. Field determination of morphological properties of aggregates such as size and shape can greatly facilitate the quality assurance/quality control (QA/QC) process for proper aggregate material selection and engineering use. Many aggregate imaging approaches have been developed to characterize the size and morphology of individual aggregates by computer vision. However, 3D field characterization of aggregate particle morphology is challenging both during the quarry
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