Research and Development of Algorithms and Methods for Constructing Three-dimensional Computer Models of Real Objects

Cover Page

Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription Access

Abstract

The article describes a technique for constructing a 3D model of an object based on the resulting images of an object using the Python programming language. As part of the study, an overview of existing solutions and an analysis of the use of algorithms for constructing three-dimensional models were performed. As a result of the work done, software was created that allows you to create a three-dimensional model based on several presented images. The scope of this work is the analysis of an object using a three-dimensional model, as well as the use of three-dimensional terrain models.

Full Text

Restricted Access

About the authors

Svetlana S. Mikhaiylova

Financial University under the Government of the Russian Federation

Author for correspondence.
Email: ssmihajlova@fa.ru
ORCID iD: 0000-0001-9183-8519

Dr. Sci. (Econ.), Associate Professor, Professor, Department of Data Analysis and Machine Learning, Faculty of Information Technology and Big Data Analysis

Russian Federation, Moscow

References

  1. Liu L., Jiang H., He P. et al. On the variance of the adaptive learning rate and beyond. URL: https://www.arXiv:1908.03265v4
  2. Subbarao R., Meer P. Projection based M-estimators. URL: https://link.springer.com/chapter/10.1007/11744023_24 (data of accesses: 14.12.2023).
  3. Structure from motion – classical implementation (full translation). URL: https://habr.com/ru/post/228525 (data of accesses: 14.12.2023).
  4. Szeliski R. Computer vision: Algorithms and applications – springer science & business media. 2010. 812 p.
  5. Tareen Sh., Zahra Kh., Zahra S. A comparative analysis of SIFT, SURF, KAZE, AKAZE, ORB, and BRISK. 10.1109/ICOMET.2018.8346440. URL: https://www.researchgate.net/publication/323561586_A_comparative_analysis_of_SIFT_SURF_KAZE_AKAZE_ORB_and_BRISK (data of accesses: 14.12.2023).
  6. Antipov I.T. Mathematical foundations of spatial analytical phototriangulation. Moscow: Kartgeocenter-Geodesizdat, 2003. 296 p. ISBN: 5-86066-055-3.
  7. Bezmenov V.M. Photogrammetry. Construction and equalization of analytical phototriangulation. Kazan.: KSU Publishing House, 2009. 86 p.
  8. Blokhinov Yu.B. Algorithms for the formation of a digital surface model and texture coating in terrestrial photogrammetry. Izvestiya vuzov. Geodesy and Aerial Photography. 2011. No. 1. Pp. 51–57. (In Rus.)
  9. Bulatnikov E.V., Goeva A.A. Comparison of computer vision libraries for use in an application using flat image recognition technology. Bulletin of the Moscow State Unitary Enterprise named after Ivan Fedorov. 2015. No. 6. Pp. 85–91. (In Rus.)
  10. Kashaganova G.B., Mergaziev K.K. Research and development of algorithms for constructing three-dimensional computer models of real objects for virtual reality systems. NAU. 2020. No. 56-1 (56). URL: https://cyberleninka.ru/article/n/issledovanie-i-razrabotka-algoritmov-postroeniya-trehmernyh-kompyuternyh-modeley-realnyh-obektov-dlya-sistem-virtualnoy-realnosti (data of accesses: 14.12.2023).
  11. Kostyuk Yu.L., Novikov Yu.L. Graph models of high-resolution color bitmaps. Bulletin of Tomsk State University. 2002. No. 275. Pp. 153–160. (In Rus.)
  12. Mashchenko P.E., Shiryaev P.P. The NetVLAD visual terrain recognition method for localizing a locomotive. Automation, Communication, Informatics. 2020. No. 10. Pp. 14–17. (In Rus.)
  13. Novikov Yu.L. Polygonal linear graph models of raster images. In: Geoinformatics-2000. Proceedings of the International Scientific and Practical Conference. A.I. Ryumkin, Yu.L. Kostyuk, A.V. Skvortsov (eds.). Tomsk: Publishing House of Tomsk University, 2000. Pp. 50–55.
  14. Stepura L.V., Demin A.Yu. Review of image vectorization methods. In: Microsoft technologies in theory and practice of programming. Proceedings of the XIII All-Russian Scientific and practical Conference of students, postgraduates and young Scientists, 2016. Scientific director A.Yu. Demin. National Research Tomsk Polytechnic University (TPU); Institute of Cybernetics. Ed. col.: A.V. Liepins et al. Tomsk: TPU Publishing House, 2016. Pp. 184–186.
  15. Point detection of the OpenCV-Python Feature 2D function (including SIFT/SURF/ORB/KAZE/FAST/BRISK/AKAZE). URL: https://russianblogs.com/article/70281143807 (data of accesses: 14.12.2023).
  16. Rosenfeld A. Image recognition and processing using computers. Transl. from English. Moscow: Mir, 1972. 230 p.
  17. Safonov A.S. Building SIFT Descriptors and finding special points in images. Izvestiya TulGU. 2017. No. 2. Pp. 182–187.

Supplementary files

Supplementary Files
Action
1. JATS XML
2. Fig. 1. The general scheme of photographing the object

Download (41KB)
3. Fig. 2. Activity diagram

Download (114KB)
4. Fig. 3. Point cloud formation code

Download (119KB)
5. Fig. 4. The algorithm of the SIFT module

Download (272KB)
6. Fig. 5. Generating a ply file

Download (166KB)
7. Fig. 6. The main window of the program

Download (13KB)
8. Fig. 7. File Selection Form

Download (52KB)
9. Fig. 8. A form with uploaded files

Download (96KB)
10. Fig. 9. Example of the generated model

Download (124KB)


This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies