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Artículos de revistas 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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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Guntamukkala, Gopi Krishna. "Shape Reconstruction in Computer Vision." International Journal of Soft Computing and Engineering (IJSCE) 12, no. 2 (2023): 1–4. https://doi.org/10.35940/ijsce.E3618.0912422.

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<strong>Abstract: </strong>The fundamental job of shape reconstruction in computer vision is crucial for many different applications, including robotics, medical imaging, and autonomous systems. The state-of-the-art methods, difficulties, and potential future directions in the field of shape reconstruction are succinctly outlined in this abstract. Inferring the three-dimensional (3D) geometry of objects or scenes from two-dimensional (2D) photographs or point clouds is the process of shape reconstruction. The conventional approaches relied on depth sensors, structure from motion, and stereo vi

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12

Brutto, M. Lo, and P. Meli. "Computer Vision Tools for 3D Modelling in Archaeology." International Journal of Heritage in the Digital Era 1, no. 1_suppl (2012): 1–6. http://dx.doi.org/10.1260/2047-4970.1.0.1.

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13

KADO, Keita, Kengo IWATA, Takuma NAKABAYASHI, and Gakuhito HIRASAWA. "PHOTOGRAPH MANAGEMENT WITH 3D CAD AND COMPUTER VISION." AIJ Journal of Technology and Design 18, no. 39 (2012): 781–84. http://dx.doi.org/10.3130/aijt.18.781.

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14

Debono, James Aaron, and Gu Fang. "Robot Learning from Demonstration Using 3D Computer Vision." Advanced Materials Research 875-877 (February 2014): 1994–99. http://dx.doi.org/10.4028/www.scientific.net/amr.875-877.1994.

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For robot application to proliferate in industry, and in unregulated environments, a simple means of programming is required. This paper describes methods for robot Learning from Demonstration (LfD). These methods used an RGB-D sensor for demonstration observation, and used finite state machines (FSMs) for policy derivation. Particularly, a method for object recognition was developed, which required only a single frame of data for training, and was able to perform real-time recognition. A planning method for object grasping was also developed. Experiments with a pick-and-place robot show that

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15

McGarrity, C., and R. L. Dalglish. "An autonomous computer peripheral for active 3D vision." Measurement Science and Technology 7, no. 11 (1996): 1591–604. http://dx.doi.org/10.1088/0957-0233/7/11/008.

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16

Yang, Jiancheng, and Bingbing Ni. "Advances and challenges in medical 3D computer vision." Journal of Image and Graphics 25, no. 10 (2020): 2002–12. http://dx.doi.org/10.11834/jig.200244.

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17

Leberl, Franz, Horst Bischof, Thomas Pock, Arnold Irschara, and Stefan Kluckner. "Aerial Computer Vision for a 3D Virtual Habitat." Computer 43, no. 6 (2010): 24–31. http://dx.doi.org/10.1109/mc.2010.156.

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18

Woodfill, John Iselin, Ron Buck, Dave Jurasek, Gaile Gordon, and Terrance Brown. "3D Vision: Developing an Embedded Stereo-Vision System." Computer 40, no. 5 (2007): 106–8. http://dx.doi.org/10.1109/mc.2007.151.

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19

Lo Brutto, Mauro, and Gino Dardanelli. "Vision metrology and Structure from Motion for archaeological heritage 3D reconstruction: a Case Study of various Roman mosaics." ACTA IMEKO 6, no. 3 (2017): 35. http://dx.doi.org/10.21014/acta_imeko.v6i3.458.

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<p class="Abstract">Vision metrology and computer vision can be successfully used for archaeological heritage 3D reconstruction in very high precision 3D measurement projects. Of those archaeological objects requiring very accurate measurements (&lt;1 mm), ancient mosaics comprise some of the most important. The aim of this paper is to assess the photogrammetric/computer vision approach in a vision metrology context as part of a 3D mosaics survey. In order to evaluate the optimal photogrammetric/computer vision workflow in this work, three different surveys were performed on three mo

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20

Miyashita, Leo, Satoshi Tabata, and Masatoshi Ishikawa. "High-Speed 3D Vision Based on Structured Light Methods." Metrology 5, no. 2 (2025): 24. https://doi.org/10.3390/metrology5020024.

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Three-dimensional measurement technologies based on computer vision have been developed with the aim of achieving perceptual speeds equivalent to humans (30 fps). However, in a highly mechanized society, there is no need for computers and robots to work slowly to match the speed of human perception. From this kind of circumstance, high-speed 3D vision with speeds far beyond that of humans, such as 1000 fps, has emerged. High-speed 3D measurement has great applicability not only for accurately recognizing a moving and deforming target but also for enabling real-time feedback, such as manipulati

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21

Jiang, Yan, and Jian Tang. "Application of 3D Stereo Visualization System in Virtual Architectural Design." Applied Mechanics and Materials 174-177 (May 2012): 1706–9. http://dx.doi.org/10.4028/www.scientific.net/amm.174-177.1706.

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The application of virtual reality in architectural design has been the focus of attention. But the true 3D sense of immersion is still a dream for the most designers and clients when they watch the computer screen. A new computer stereo vision imaging system was constructed to help us obtain 3D visual of architectures and the implementation process became easier. And the application of advanced three-dimensional vision technology offered a more scientific, more interesting means of display and human-computer interaction. By using 3D stereo vision imaging system, all designers and users can ob

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22

Zeng, Bo Xia, and Wen Feng Li. "A 3D Dynamic Face Recognition Method Based on Computer Vision." Applied Mechanics and Materials 556-562 (May 2014): 5006–8. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.5006.

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The non-rigid 3D characters recovery technology for 2D images array is affected by background diversity, motion complexity, data losing and noise of feature points, so the recognition and recovery accuracy of facial features deformation is low. Due to the high error in traditional method, the paper puts forward a 3D facial recognition algorithm based on random images array, which converts the 2D features to 3D by nonlinear mapping, and completes the recognition on foundation of 3D geometric features distance. The experimental results show that the method effectively reduces error and improves

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23

Kumari, M., Som veer, RR Deshmukh, RV Vinchurkar, and PL Parameswari. "Computer Vision Driven Precision Dairy Farming for Efficient Cattle Management." 3 2, no. 3 (2023): 47–54. http://dx.doi.org/10.46632/jdaai/2/3/9.

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Precision Dairy Farming (PDF)” or “The Per Animal Approach” can be enhanced through the implementation of three-dimensional computer vision, which offers improved cattle identification, disease monitoring, and growth assessment. The integration of 3D vision systems is particularly vital for advancing dairy farming practices in the next generation. These systems facilitate the automation of various animal husbandry tasks, including monitoring, herding, feeding, milking, and bedding of animals. The applications of 3D computer vision in PLF encompass diverse platforms, such as 3D camera installat

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24

Safyari, Yashar, Masoud Mahdianpari, and Hodjat Shiri. "A Review of Vision-Based Pothole Detection Methods Using Computer Vision and Machine Learning." Sensors 24, no. 17 (2024): 5652. http://dx.doi.org/10.3390/s24175652.

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Potholes and other road surface damages pose significant risks to vehicles and traffic safety. The current methods of in situ visual inspection for potholes or cracks are inefficient, costly, and hazardous. Therefore, there is a pressing need to develop automated systems for assessing road surface conditions, aiming to efficiently and accurately reconstruct, recognize, and locate potholes. In recent years, various methods utilizing (a) computer vision, (b) three-dimensional (3D) point clouds, or (c) smartphone data have been employed to map road surface quality conditions. Machine learning and

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25

Karanam, Madhavi, Varun Kumar Kamani, Vikas Kuchana, Gopal Krishna Reddy Koppula, and Gautham Gongada. "Object and it’s dimension detection in real time." E3S Web of Conferences 391 (2023): 01016. http://dx.doi.org/10.1051/e3sconf/202339101016.

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Object and its dimension detection from images and videos can be very helpful for everyday use. This paper discusses the use of the system to detect an object in real time and provide its dimensions upon demand. The object dimension measurement and detection are some of the important topics of computer vision which helps in automating the manual tasks. Human beings are capable of recognizing and spotting objects in images and videos, but computers lack that ability with out prior training. To train the computer, we must use machine learning, computer vision, and object detection algorithms. Th

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26

Bin Sulong, Ghazali, and M . Randles. "Computer Vision Using Pose Estimation." Wasit Journal of Computer and Mathematics Science 2, no. 1 (2023): 85–92. http://dx.doi.org/10.31185/wjcm.111.

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Pose estimation involves estimating the position and orientation of objects in a 3D space, and it has applications in areas such as robotics, augmented reality, and human-computer interaction. There are several methods for pose estimation, including model-based, feature-based, direct, hybrid, and deep learning-based methods. Each method has its own strengths and weaknesses, and the choice of method depends on the specific requirements of the application, object being estimated, and available data. Advancements in computer vision and machine learning have made it possible to achieve high accura

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27

Lu, Juan. "Construction of Interactive English Translation Teaching Mode Based on 3D Computer Vision Technology Algorithm." International Journal of Web-Based Learning and Teaching Technologies 20, no. 1 (2025): 1–19. https://doi.org/10.4018/ijwltt.380661.

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This paper reflected on the traditional lecture teaching method and proposed an alternative English translation teaching method based on 3D computer vision technology to increase student interest in translation, improve students' translation ability, and compensate for the shortcomings of traditional translation teaching methods. Through experimental research, this paper found that the interactive English translation teaching mode based on 3D computer vision technology was conducive to improving students' translation speed, translation accuracy, comprehensive competence, and the quality of tra

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28

Gong, Lin. "Simulating 3D Cloud Shape Based on Computer Vision and Particle System." Applied Mechanics and Materials 182-183 (June 2012): 819–22. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.819.

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Clouds are an important part of natural environment. The realistic simulation of cloud is a challenging topic in computer graphics. This paper proposes a simple, efficient approach based on computer vision and particle system to model various 3D clouds. This method use computer vision technology to extract 3D structure information of clouds from images, then using particles technology to fill the 3D space and render the cloud. This method is suitable to model all kinds of clouds, such as stratus, cumulus, cirrus etc. It is an improvement over earlier systems that modeled only one type of cloud

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29

Dai, Yue. "Semantic Detection of Vehicle Violation Video Based on Computer 3D Vision." Advances in Multimedia 2022 (April 9, 2022): 1–6. http://dx.doi.org/10.1155/2022/5283191.

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In order to study the semantic detection accuracy of 3D vehicle accident video, an accident detection method combining 2D image and 3D information was proposed. The 3D semantic bounding box generated by the 3D detection and tracking task of the vehicle is used to extract the motion features of the vehicle, it includes the trajectory of the vehicle and the dimension and orientation of the 3D bounding frame, and the 3D semantic bounding frame is used to establish the evaluation index of the accident detection. The experimental results show that the average loss function of each batch of 1000 ima

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30

Sankari, Mrs K. Siva, and Dr B. Sathya Bama. "Literature Analysis on Three Dimensional Object Recognition using Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 02 (2024): 1–10. http://dx.doi.org/10.55041/ijsrem28558.

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The subject of 3D object identification (segmentation, detection, and classification) has received much research in the areas of computer vision, graphics, and machine learning. Recently, deep learning algorithms have surpassed traditional methods for 3D segmentation problems because of their success in 2D computer vision. As a result, a number of novel methods have been developed and tested on a range of gold-standard datasets.In order for the pattern recognition system to correctly identify the item, the features must be extracted in a form that is compatible with the chosen identification t

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31

Ioannidou, Anastasia, Elisavet Chatzilari, Spiros Nikolopoulos, and Ioannis Kompatsiaris. "Deep Learning Advances in Computer Vision with 3D Data." ACM Computing Surveys 50, no. 2 (2017): 1–38. http://dx.doi.org/10.1145/3042064.

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32

Mortensen, Anders Krogh, Pavel Lisouski, and Peter Ahrendt. "Weight prediction of broiler chickens using 3D computer vision." Computers and Electronics in Agriculture 123 (April 2016): 319–26. http://dx.doi.org/10.1016/j.compag.2016.03.011.

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33

Obukhov, A. D., D. L. Dedov, E. O. Surkova, and I. L. Korobova. "3D Human Motion Capture Method Based on Computer Vision." Advanced Engineering Research (Rostov-on-Don) 23, no. 3 (2023): 317–28. http://dx.doi.org/10.23947/2687-1653-2023-23-3-317-328.

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Introduction. The analysis of approaches to tracking the human body identified problems when capturing movements in a three-dimensional coordinate system. The prospects of motion capture systems based on computer vision are noted. In existing studies on markerless motion capture systems, positioning is considered only in two-dimensional space. Therefore, the research objective is to increase the accuracy of determining the coordinates of the human body in three-dimensional coordinates through developing a motion capture method based on computer vision and triangulation algorithms.Materials and

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34

Tumurbaatar, Tserennadmid, and Taejung Kim. "Comparative Study of Relative-Pose Estimations from a Monocular Image Sequence in Computer Vision and Photogrammetry." Sensors 19, no. 8 (2019): 1905. http://dx.doi.org/10.3390/s19081905.

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Techniques for measuring the position and orientation of an object from corresponding images are based on the principles of epipolar geometry in the computer vision and photogrammetric fields. Contributing to their importance, many different approaches have been developed in computer vision, increasing the automation of the pure photogrammetric processes. The aim of this paper is to evaluate the main differences between photogrammetric and computer vision approaches for the pose estimation of an object from image sequences, and how these have to be considered in the choice of processing techni

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35

Zhou, Linglong, Guoxin Wu, Yunbo Zuo, Xuanyu Chen, and Hongle Hu. "A Comprehensive Review of Vision-Based 3D Reconstruction Methods." Sensors 24, no. 7 (2024): 2314. http://dx.doi.org/10.3390/s24072314.

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With the rapid development of 3D reconstruction, especially the emergence of algorithms such as NeRF and 3DGS, 3D reconstruction has become a popular research topic in recent years. 3D reconstruction technology provides crucial support for training extensive computer vision models and advancing the development of general artificial intelligence. With the development of deep learning and GPU technology, the demand for high-precision and high-efficiency 3D reconstruction information is increasing, especially in the fields of unmanned systems, human-computer interaction, virtual reality, and medi

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36

Lu, Luanhao. "A Survey of Learning Approaches and Application for 3D Vision." MATEC Web of Conferences 173 (2018): 03053. http://dx.doi.org/10.1051/matecconf/201817303053.

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Three-dimensional (3D) vision extracted from the stereo images or reconstructed from the two-dimensional (2D) images is the most effective topic in computer vision and video surveillance. Three-dimensional scene is constructed through two stereo images which existing disparity map by Stereo vision. Many methods of Stereo matching which contains median filtering, mean-shift segmentation, guided filter and joint trilateral filters [1] are used in many algorithms to construct the precise disparity map. These methods committed to figure out the image synthesis range in different Stereo matching fi

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37

Vinodkumar, Prasoon Kumar, Dogus Karabulut, Egils Avots, Cagri Ozcinar, and Gholamreza Anbarjafari. "A Survey on Deep Learning Based Segmentation, Detection and Classification for 3D Point Clouds." Entropy 25, no. 4 (2023): 635. http://dx.doi.org/10.3390/e25040635.

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The computer vision, graphics, and machine learning research groups have given a significant amount of focus to 3D object recognition (segmentation, detection, and classification). Deep learning approaches have lately emerged as the preferred method for 3D segmentation problems as a result of their outstanding performance in 2D computer vision. As a result, many innovative approaches have been proposed and validated on multiple benchmark datasets. This study offers an in-depth assessment of the latest developments in deep learning-based 3D object recognition. We discuss the most well-known 3D

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38

Sapirstein, Philip. "Human versus computer vision in archaeological recording." Studies in Digital Heritage 4, no. 2 (2021): 134–59. http://dx.doi.org/10.14434/sdh.v4i2.31520.

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As 3D scanning and photogrammetry are supplanting traditional illustration techniques with increasing speed, archaeologists and architectural historians have sounded alarms about what stands to be lost if hand drawing is altogether eliminated from fieldwork. This paper argues that the most direct threat is to a particular form of archaeological illustration which does not necessarily share the advantages attributed to other kinds of drawing. Recording by means of “technical drawing” communicates a collectively agreed interpretation of the ancient record, and its primary benefit is not stimulat

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39

Heickal, Hasnain, Tao Zhang, and Md Hasanuzzaman. "Computer Vision-Based Real-Time 3D Gesture Recognition Using Depth Image." International Journal of Image and Graphics 15, no. 01 (2015): 1550004. http://dx.doi.org/10.1142/s0219467815500047.

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Gesture is one of the fundamental ways of human machine natural interaction. To understand gesture, the system should be able to interpret 3D movements of human. This paper presents a computer vision-based real-time 3D gesture recognition system using depth image which tracks 3D joint position of head, neck, shoulder, arms, hands and legs. This tracking is done by Kinect motion sensor with OpenNI API and 3D motion gesture is recognized using the movement trajectory of those joints. User to Kinect sensor distance is adapted using proposed center of gravity (COG) correction method and 3D joint p

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Tornow, M., J. Kaszubiak, R. W. Kuhn, B. Michaelis, and G. Krell. "Stereophotogrammetric real-time 3D machine vision." Pattern Recognition and Image Analysis 16, no. 1 (2006): 100–103. http://dx.doi.org/10.1134/s1054661806010317.

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Tornow, M., J. Kaszubiak, R. W. Kuhn, B. Michaelis, and G. Krell. "Stereophotogrammetric 3D real-time machine vision." Pattern Recognition and Image Analysis 18, no. 1 (2008): 139–50. http://dx.doi.org/10.1134/s1054661808010173.

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42

Hoang, Long, Suk-Hwan Lee, Oh-Heum Kwon, and Ki-Ryong Kwon. "A Deep Learning Method for 3D Object Classification Using the Wave Kernel Signature and A Center Point of the 3D-Triangle Mesh." Electronics 8, no. 10 (2019): 1196. http://dx.doi.org/10.3390/electronics8101196.

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Computer vision recently has many applications such as smart cars, robot navigation, and computer-aided manufacturing. Object classification, in particular 3D classification, is a major part of computer vision. In this paper, we propose a novel method, wave kernel signature (WKS) and a center point (CP) method, which extracts color and distance features from a 3D model to tackle 3D object classification. The motivation of this idea is from the nature of human vision, which we tend to classify an object based on its color and size. Firstly, we find a center point of the mesh to define distance

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43

Chang, Yong, Wen Peng Xu, and Lei Wang. "Research on 3D Visualization of Underground Antique Tomb Based on Augmented Reality." Applied Mechanics and Materials 336-338 (July 2013): 1434–38. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.1434.

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This paper researches on 3D visualization of underground antique tomb based on Augmented Reality .At first,this paper established 3D model of antique tomb with OpenGL in VC++6.0. In order to establish augmented reality 3D model of antique tomb, the OpenGL model of antique tomb was transferred into ARToolKit and realized 3D augmented reality visualization in ARToolKit. As ARToolKit uses computer vision techniques to calculate the real camera position and orientation relative to marked cards,so it is difficult to provide AR applications in outdoor environment. At last, this paper puts forth a ne

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44

Brito Carcaño, Jesús E., Stéphane Cuenat, Belal Ahmad, et al. "Digital holographic microscopy applied to 3D computer microvision by using deep neural networks." EPJ Web of Conferences 287 (2023): 13011. http://dx.doi.org/10.1051/epjconf/202328713011.

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Deep neural networks are increasingly applied in many branches of applied science such as computer vision and image processing by increasing performances of instruments. Different deep architectures such as convolutional neural networks or Vision Transformers can be used in advanced coherent imaging techniques such as digital holography to extract various metrics such as autofocusing reconstruction distance or 3D position determination in order to target automated microscopy or real-time phase image restitution. Deep neural networks can be trained with both datasets simulated and experimental

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Knyaz, V. A., A. A. Maksimov, and M. M. Novikov. "VISION BASED AUTOMATED ANTHROPOLOGICAL MEASUREMENTS AND ANALYSIS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W12 (May 9, 2019): 117–22. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w12-117-2019.

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<p><strong>Abstract.</strong> Modern techniques of optical 3D measurements such as photogrammetry, computer tomography, laser 3D scanning provide new possibilities for acquiring accurate 2D and 3D data of high resolution, thus creating new conditions for anthropological data analysis. Traditional anthropological manual point measurements can be substituted by analysis of accurate textured 3D models, which allow to retrieve more information about studied object and easily to share data for independent analysis. The paper presents the vision-based techniques for anthropological

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Guo, Yulan, Hanyun Wang, Ronald Clark, Stefano Berretti, and Mohammed Bennamoun. "Deep learning for 3D vision." IET Computer Vision 16, no. 7 (2022): 567–69. http://dx.doi.org/10.1049/cvi2.12141.

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Krishna, Guntamukkala Gopi. "Shape Reconstruction in Computer Vision." International Journal of Soft Computing and Engineering 12, no. 4 (2022): 1–5. http://dx.doi.org/10.35940/ijsce.e3618.0912422.

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The fundamental job of shape reconstruction in computer vision is crucial for many different applications, including robotics, medical imaging, and autonomous systems. The state-of-the-art methods, difficulties, and potential future directions in the field of shape reconstruction are succinctly outlined in this abstract. Inferring the three-dimensional (3D) geometry of objects or scenes from two-dimensional (2D) photographs or point clouds is the process of shape reconstruction. The conventional approaches relied on depth sensors, structure from motion, and stereo vision. Convolutional neural

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Preising, B., and T. C. Hisa. "Robot performance measurement and calibration using a 3D computer vision system." Robotica 13, no. 4 (1995): 327–37. http://dx.doi.org/10.1017/s0263574700018762.

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SummaryPresent day robot systems are manufactured to perform within industry accepted tolerances. However, to use such systems for tasks requiring high precision, various methods of robot calibration are generally required. These procedures can improve the accuracy of a robot within a small volume of the robot's workspace. The objective of this paper is to demonstrate the use of a single camera 3D computer vision system as a position sensor in order to perform robot calibration. A vision feedback scheme, termed Vision-guided Robot Control (VRC), is described which can improve the accuracy of a

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Liu, Dan. "3D Face Geometry Optimization Using Artificial Intelligence and Computer Graphics." Scientific Programming 2022 (March 22, 2022): 1–9. http://dx.doi.org/10.1155/2022/9959153.

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Human face plays an indispensable role in emotional expression and information exchange, which attracts large number of researchers to study face recognition. Nowadays, with the rapid development of computer graphics, artificial intelligence, and other technologies, the ability of the human vision system to recognize facial expressions and facial organs is enhanced. More and more experts think about how to make the computer vision system have this capability. By combining artificial intelligence and computer graphics, this paper studies how to optimize the 3D face network model and extract geo

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Adán, Antonio, Víctor Pérez, José-Luis Vivancos, Carolina Aparicio-Fernández, and Samuel A. Prieto. "Proposing 3D Thermal Technology for Heritage Building Energy Monitoring." Remote Sensing 13, no. 8 (2021): 1537. http://dx.doi.org/10.3390/rs13081537.

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The energy monitoring of heritage buildings has, to date, been governed by methodologies and standards that have been defined in terms of sensors that record scalar magnitudes and that are placed in specific positions in the scene, thus recording only some of the values sampled in that space. In this paper, however, we present an alternative to the aforementioned technologies in the form of new sensors based on 3D computer vision that are able to record dense thermal information in a three-dimensional space. These thermal computer vision-based technologies (3D-TCV) entail a revision and updati

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