Uniqueness theorems for recovering the inverse image under~degenerate transformations
Izvestiya of Saratov University. Mathematics. Mechanics. Informatics, Tome 22 (2022) no. 1, pp. 15-27.

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When solving problems of three-dimensional reconstruction of objects from images, the problem of determining the conditions under which such a reconstruction will have one or another degree of uniqueness is relevant. It is these conditions that make it possible to apply, in particular, deep machine learning methods using convolutional neural networks to determine the spatial orientation of objects or their constituent parts. From a mathematical point of view, the problem is reduced to determining the conditions for restoring the preimage for transforming the projection. In this article, we prove a number of uniqueness theorems for this kind of restoration. In particular, it has been proved that the parameters of a rotation transformation close to identical can be uniquely determined from the projection of the result of such rotation of an object with a given structure. In addition, the article found the conditions under which the spatial orientation of an object can be calculated from its projection.
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A. A. Klyachin; V. A. Klyachin. Uniqueness theorems for recovering the inverse image under~degenerate transformations. Izvestiya of Saratov University. Mathematics. Mechanics. Informatics, Tome 22 (2022) no. 1, pp. 15-27. http://geodesic.mathdoc.fr/item/ISU_2022_22_1_a1/

[1] Mousavian A., Anguelov D., Flynn J., Kosecka J., “3D Bounding Box Estimation Using Deep Learning and Geometry”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, 5632–5640 | DOI

[2] Gordeev A. Y., Klyachin V. A., “Determination of the Spatial Position of Cars on the Road Using Data from a Camera or DVR”, «Smart Technologies» for Society, State and Economy, ISC 2020, Lecture Notes in Networks and Systems, 155, eds. E. G. Popkova, B. S. Sergi, Springer, Cham, 2021, 172–180 | DOI

[3] Gordeev A. Y., Klyachin V. A., Kurbanov E. R., Driaba A. Y., “Autonomous Mobile Robot with AI Based on Jetson Nano”, Proceedings of the Future Technologies Conference, FTC 2020, v. 1, Advances in Intelligent Systems and Computing, 1288, eds. K. Arai, S. Kapoor, R. Bhatia., Springer, Cham, 2021, 190–204 | DOI

[4] Ren M., Pokrovsky A., Yang B., Urtasun R., “SBNet: Sparse Blocks Network for Fast Inference”, IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, 8711–8720 | DOI

[5] Hu H., Cai Q., Wang D., Lin J., Sun M., Krhenbhl P., Darrell T., Yu F., “Joint Monocular 3D Vehicle Detection and Tracking”, Proceedings of the IEEE/CVF International Conference on Computer Vision (Seoul, Korea, 27 October – 2 November, 2019), 5390–5399

[6] Huang S., Qi S., Zhu Y., Xiao Y., Xu Y., Zhu S. C., “Holistic 3d scene parsing and reconstruction from a single rgb image”, Proceedings of the European Conference on Computer Vision (ECCV), 2018, 187–203 | Zbl

[7] Jackson A. S., Bulat A., Argyriou V., Tzimiropoulos G., “Large pose 3D face reconstruction from a single image via direct volumetric CNN regression”, IEEE International Conference on Computer Vision (ICCV), IEEE, 2017, 1031–1039 | DOI

[8] Ferková Z., Urbanová P., Černý D., Žuži M., Matula P., “Age and gender-based human face reconstruction from single frontal image”, Multimedia Tools and Applications, 79 (2020), 3217–3242 | DOI

[9] Klyachin V. A., Grigorieva E. G., “Algorithm for automatic determination of camera orientation parameters in space based on the characteristic elements of the photograph”, Tendencii razvitiya nauki i obrazovaniya, 45:6 (2018), 10–20 (in Russian) | DOI

[10] Klyachin V. A., Grigorieva E. G., “A 3D reconstruction algorithm of a surface of revolution from its projection”, Journal of Applied and Industrial Mathematics, 14 (2020), 85–91 | DOI | DOI | MR

[11] Kamyab S., Ghodsi A., Zohreh Azimifar S., Deep structure for end-to-end inverse rendering, 2017, arXiv: 1708.08998

[12] Kamyab S., Zohreh Azimifar S., End-to-end 3D shape inverse rendering of different classes of objects from a single input image, 2017, arXiv: 1711.05858

[13] Penczek P. A., “Fundamentals of three-dimensional reconstruction from projections”, Methods in Enzymology, 2010, no. 482, 1–33 | DOI