<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.3 20210610//EN" "JATS-journalpublishing1-3.dtd">
<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">caht</journal-id><journal-title-group><journal-title xml:lang="ru">Научный вестник МГТУ ГА</journal-title><trans-title-group xml:lang="en"><trans-title>Civil Aviation High Technologies</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">2079-0619</issn><issn pub-type="epub">2542-0119</issn><publisher><publisher-name>Moscow State Technical University of Civil Aviation (MSTU CA)</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26467/2079-0619-2023-26-5-30-41</article-id><article-id custom-type="elpub" pub-id-type="custom">caht-2231</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>ТРАНСПОРТНЫЕ СИСТЕМЫ</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>TRANSPORTATION SYSTEMS</subject></subj-group></article-categories><title-group><article-title>Обеспечение визуальной когерентности в обучающих системах дополненной реальности с учетом авиакосмической специфики</article-title><trans-title-group xml:lang="en"><trans-title>Visual coherence in augmented reality training systems considering aerospace specific features</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Горбунов</surname><given-names>А. Л.</given-names></name><name name-style="western" xml:lang="en"><surname>Gorbunov</surname><given-names>A. L.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Горбунов Андрей Леонидович, кандидат технических наук, доцент, доцент кафедры управления воздушным движением</p><p>Москва</p></bio><bio xml:lang="en"><p>Andrey L. Gorbunov, Candidate of Technical Sciences, Associate Professor, Associate Professor of the Air Traffic Management Chair</p><p>Moscow</p></bio><email xlink:type="simple">a.gorbunov@mstuca.aero</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Ли</surname><given-names>Ю.</given-names></name><name name-style="western" xml:lang="en"><surname>Li</surname><given-names>Yu.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Ли Юньхань, аспирантка</p><p>Москва</p></bio><bio xml:lang="en"><p>Yunhan Li, Postgraduate Student</p><p>Moscow</p></bio><email xlink:type="simple">antatanoe@gmail.com</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Московский государственный технический университет гражданской авиации</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Moscow State Technical University of Civil Aviation</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2023</year></pub-date><pub-date pub-type="epub"><day>30</day><month>10</month><year>2023</year></pub-date><volume>26</volume><issue>5</issue><fpage>30</fpage><lpage>41</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Горбунов А.Л., Ли Ю., 2023</copyright-statement><copyright-year>2023</copyright-year><copyright-holder xml:lang="ru">Горбунов А.Л., Ли Ю.</copyright-holder><copyright-holder xml:lang="en">Gorbunov A.L., Li Y.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://avia.mstuca.ru/jour/article/view/2231">https://avia.mstuca.ru/jour/article/view/2231</self-uri><abstract><p>В мае 2022 года саудовская правительственная структура Saudi Arabian Military Industries приобрела обучающую платформу дополненной реальности для летчиков, в сентябре корпорация Boeing начала разработку тренажера пилота дополненной реальности, в ноябре стартовал аналогичный проект ведущего британского разработчика авиационной техники BAE Systems. Эти факты позволяют уверенно говорить о начале новой эпохи авиационных тренажеров – тренажеров с применением технологии дополненной реальности. Одно из перспективных преимуществ данной технологии – возможность безопасного моделирования опасных ситуаций в реальном мире. Необходимым условием использования этого преимущества является обеспечение визуальной когерентности сцен дополненной реальности: виртуальные объекты должны быть неотличимы от реальных. Все мировые IT-лидеры рассматривают дополненную реальность как следующую «большую волну» радикальных изменений в цифровой электронике, поэтому визуальная когерентность становится ключевым вопросом для будущего IT, а в аэрокосмических приложениях визуальная когерентность уже приобрела практическое значение. В РФ имеет место серьезное отставание в изучении проблематики визуальной когерентности в целом и для авиатренажеров дополненной реальности в частности: на момент публикации авторам удалось обнаружить в российском научном пространстве только две работы по теме, тогда как за рубежом их число уже около тысячи. Цель настоящей обзорной статьи – создать условия для купирования проблемы. Визуальная когерентность зависит от многих факторов: освещения, цветового тона, теней от виртуальных объектов на реальных, взаимных отражений, текстур виртуальных поверхностей, оптических аберраций, конвергенции и аккомодации и др. В статье анализируются публикации, посвященные методам оценки условий освещенности и цветового тона реальной сцены и переноса таковых на виртуальные объекты с использованием зондов и по отдельным изображениям, а также по рендерингу виртуальных объектов в сценах дополненной реальности, в том числе с применением нейросетей.</p></abstract><trans-abstract xml:lang="en"><p>In May 2022, Saudi Arabian Military Industries, a Saudi government agency, acquired an augmented reality training platform for pilots. In September, the Boeing Corporation began the development of an augmented reality pilot simulator. In November, a similar project was launched by BAE Systems, a leading British developer of aeronautical engineering. These facts allow us to confidently speak about the beginning of a new era of aviation simulators – simulators using the augmented reality technology. One of the promising advantages of this technology is the ability to safely simulate dangerous situations in the real world. A necessary condition for using this advantage is to ensure the visual coherence of augmented reality scenes: virtual objects must be indistinguishable from real ones. All the global IT leaders consider augmented reality as the subsequent surge of radical changes in digital electronics, so visual coherence is becoming a key issue for the future of IT, and in aerospace applications, visual coherence has already acquired practical significance. The Russian Federation lags far behind in studying the problems of visual coherence in general and for augmented reality flight simulators in particular: at the time of publication the authors managed to find only two papers on the subject in the Russian research space, while abroad their number is already approximately a thousand. The purpose of this review article is to create conditions for solving the problem. Visual coherence depends on many factors: lighting, color tone, shadows from virtual objects on real ones, mutual reflections, textures of virtual surfaces, optical aberrations, convergence and accommodation, etc. The article reviews the publications devoted to methods for assessing the conditions of illumination and color tone of a real scene and transferring them to virtual objects using various probes and by individual images, as well as by rendering virtual objects in augmented reality scenes, using neural networks.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>авиационные тренажеры</kwd><kwd>дополненная реальность</kwd><kwd>визуальная когерентность</kwd></kwd-group><kwd-group xml:lang="en"><kwd>flight simulators</kwd><kwd>augmented reality</kwd><kwd>visual coherence</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Горбунов А.Л. Визуальная когерентность в дополненной реальности // Advanced Engineering Research. 2023. № 2. С. 180–190. DOI: 10.23947/2687-1653-2023-23-2-180-190</mixed-citation><mixed-citation xml:lang="en">Gorbunov, А.L. (2023). Visual coherence for augmented reality. Advanced Engineering Research, no. 2, pp. 180–190. DOI: 10.23947/2687-1653-2023-23-2-180-190 (in Russian)</mixed-citation></citation-alternatives></ref><ref id="cit2"><label>2</label><citation-alternatives><mixed-citation xml:lang="ru">Горбунов А.Л. Визуальная однородность сцен дополненной реальности // Запись и воспроизведение объемных изображений в кинематографе и других областях: сборник докладов IX Международной научно-практической конференции. Москва, 17–18 апреля 2017 г. М.: ВГИК им. С.А. Герасимова, 2017. С. 235–239.</mixed-citation><mixed-citation xml:lang="en">Gorbunov, А.L. (2017). Visual homogeneity of augmented reality scenes. In: Zapis i vosproizvedeniye obyemnykh izobrazheniy v kinematografe i drugikh oblastyakh: sbornik dokladov IX Mezhdunarodnoy nauchno-prakticheskoy konferentsii. Moscow: VGIK im. S.A. Gerasimova, pp. 235–239. (in Russian)</mixed-citation></citation-alternatives></ref><ref id="cit3"><label>3</label><citation-alternatives><mixed-citation xml:lang="ru">Hughes C. The psychometrics of cybersickness in augmented reality / C. Hughes, C. Fidopiastis, K. Stanney, P. Bailey, E. Ruiz [Электронный ресурс] // Frontiers in Virtual Reality. 2020. Vol. 1. ID: 602954. DOI: 10.3389/frvir.2020.602954 (дата обращения: 05.04.2023).</mixed-citation><mixed-citation xml:lang="en">Hughes, C., Fidopiastis, C., Stanney, K., Bailey, P., Ruiz, E. (2020). The psychometrics of cybersickness in augmented reality. Frontiers in Virtual Reality, vol. 1, ID: 602954. DOI: 10.3389/frvir.2020.602954 (accessed: 05.04.2023).</mixed-citation></citation-alternatives></ref><ref id="cit4"><label>4</label><citation-alternatives><mixed-citation xml:lang="ru">Somanath G., Kurz D. HDR environment map estimation for real-time augmented reality [Электронный ресурс] // IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). USA: Nashville, TN, 2021. Pp. 11293–11301. DOI: 10.1109/CVPR46437.2021.01114 (дата обращения: 05.04.2023).</mixed-citation><mixed-citation xml:lang="en">Somanath, G., Kurz, D. (2021). HDR environment map estimation for real-time augmented reality. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, pp. 11293–11301. DOI: 10.1109/CVPR46437.2021.01114 (accessed: 05.04.2023).</mixed-citation></citation-alternatives></ref><ref id="cit5"><label>5</label><citation-alternatives><mixed-citation xml:lang="ru">Zollmann S. Visualization techniques in augmented reality: A taxonomy, methods and patterns / S. Zollmann, T. Langlotz, R. Grasset, W.H. Lo, S. Mori, H. Regenbrecht // IEEE Transactions on Visualization and Computer Graphics, 2021. Vol. 27, no. 9. Pp. 3808–3825. DOI: 10.1109/TVCG.2020.2986247</mixed-citation><mixed-citation xml:lang="en">Zollmann, S., Langlotz, T., Grasset, R., Lo, W.H., Mori, S., Regenbrecht, H. (2021). Visualization techniques in augmented reality: A taxonomy, methods and patterns. In: IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 9, pp. 3808–3825. DOI: 10.1109/TVCG.2020.2986247</mixed-citation></citation-alternatives></ref><ref id="cit6"><label>6</label><citation-alternatives><mixed-citation xml:lang="ru">Kronander J. Photorealistic rendering of mixed reality scenes / J. Kronander, F. Banterle, A. Gardner, E. Miandji, J. Unger // Computer Graphics Forum. 2015. Vol. 34, iss. 2. Pp. 643–665. DOI: 10.1111/cgf.12591</mixed-citation><mixed-citation xml:lang="en">Kronander, J., Banterle, F., Gardner, A., Miandji, E., Unger, J. (2015). Photorealistic rendering of mixed reality scenes. Computer Graphics Forum, vol. 34, issue 2, pp. 643–665. DOI: 10.1111/cgf.12591</mixed-citation></citation-alternatives></ref><ref id="cit7"><label>7</label><citation-alternatives><mixed-citation xml:lang="ru">Debevec P. A single-shot light probe / P. Debevec, P. Graham, J. Busch, M. Bolas [Электронный ресурс] // SIGGRAPH '12: Special Interest Group on Computer Graphics and Interactive Techniques Conference. California, Los Angeles, 2012. Article No.: 10. Pp. 1–19. DOI: 10.1145/2343045.2343058 (дата обращения: 05.04.2023).</mixed-citation><mixed-citation xml:lang="en">Debevec, P., Graham, P., Busch, J., Bolas, M. (2012). A single-shot light probe. In SIGGRAPH '12: Special Interest Group on Computer Graphics and Interactive Techniques Conference, Los Angeles California, Article No.: 10, pp. 1–19. DOI: 10.1145/2343045.2343058 (accessed: 05.04.2023).</mixed-citation></citation-alternatives></ref><ref id="cit8"><label>8</label><citation-alternatives><mixed-citation xml:lang="ru">Unger J. Capturing and rendering with incident light fields / J. Unger, A. Wenger, T. Hawkins, A. Gardner, P. Debevec // 14th Eurographics Symposium on Rendering, 2003. Pp. 141–149. DOI: 10.2312/EGWR/EGWR03/141-149</mixed-citation><mixed-citation xml:lang="en">Unger, J., Wenger, A., Hawkins, T., Gardner, A., Debevec, P. (2003). Capturing and rendering with incident light fields. In: 14th Eurographics Symposium on Rendering, pp. 141–149. DOI: 10.2312/EGWR/EGWR03/141-149</mixed-citation></citation-alternatives></ref><ref id="cit9"><label>9</label><citation-alternatives><mixed-citation xml:lang="ru">Alhakamy A., Tuceryan M. CubeMap360: Interactive global illumination for augmented reality in dynamic environment // Proceedings of IEEE SoutheastCon. USA, Huntsville, AL, 2019. Pp. 1–8. DOI: 10.1109/SoutheastCon42311.2019.9020588</mixed-citation><mixed-citation xml:lang="en">Alhakamy, A., Tuceryan, M. (2019). CubeMap360: Interactive global illumination for augmented reality in dynamic environment. In: Proceedings of IEEE SoutheastCon, Huntsville, AL, USA, pp. 1–8. DOI: 10.1109/SoutheastCon42311.2019.9020588</mixed-citation></citation-alternatives></ref><ref id="cit10"><label>10</label><citation-alternatives><mixed-citation xml:lang="ru">Knorr S., Kurz D. Real-time illumination estimation from faces for coherent rendering // 2014 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). Germany, Munich, 2014. Pp. 349–350. DOI: 10.1109/ISMAR.2014.6948483</mixed-citation><mixed-citation xml:lang="en">Knorr, S., Kurz, D. (2014). Real-time illumination estimation from faces for coherent rendering. In: 2014 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), Munich, Germany, pp. 349–350. DOI: 10.1109/ISMAR.2014.6948483</mixed-citation></citation-alternatives></ref><ref id="cit11"><label>11</label><citation-alternatives><mixed-citation xml:lang="ru">Karsch K., Sunkavalli K., Hadap S. и др. Automatic scene inference for 3D object compositing // ACM Transactions on Graphics. 2014. Vol. 33, no. 3. Pp. 1–15. DOI: 10.1145/2602146</mixed-citation><mixed-citation xml:lang="en">Karsch, K., Sunkavalli, K., Hadap, S., et al. (2014). Automatic scene inference for 3D object compositing. ACM Transactions on Graphics, vol. 33, no. 3, pp. 1–15. DOI: 10.1145/2602146</mixed-citation></citation-alternatives></ref><ref id="cit12"><label>12</label><citation-alternatives><mixed-citation xml:lang="ru">Tsunezaki S. Reproducing material appearance of real objects using mobile augmented reality / S. Tsunezaki, R. Nomura, T. Komuro, S. Yamamoto, N. Tsumura // 2018 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct). Germany, Munich, 2018. Pp. 196–197. DOI: 10.1109/ISMAR-Adjunct.2018.00065</mixed-citation><mixed-citation xml:lang="en">Tsunezaki, S., Nomura, R., Komuro, T., Yamamoto, S., Tsumura, N. (2018). Reproducing material appearance of real objects using mobile augmented reality. In: 2018 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), Munich, Germany, pp. 196–197. DOI: 10.1109/ISMAR-Adjunct.2018.00065</mixed-citation></citation-alternatives></ref><ref id="cit13"><label>13</label><citation-alternatives><mixed-citation xml:lang="ru">Reinhard E. Real-time color blending of rendered and captured video / E. Reinhard, A.O. Akyüz, M. Colbert, C. Hughes, M. Oconnor [Электронный ресурс] // Proceedings Interservice/Industry Training, Simulation and Education Conference (I/ITSEC). Orlando, Amerika Birleşik Devletleri, 2004. P. 15021. URL: http://www.ceng.metu.edu.tr/~akyuz/files/blend.pdf (дата обращения: 05.04.2023).</mixed-citation><mixed-citation xml:lang="en">Reinhard, E., Akyuz, A.O., Colbert, M., Hughes, C., O’Сonnor, M. (2004). Real-time color blending of rendered and captured video. In: Proceedings Interservice/Industry Training, Simulation and Education Conference (I/ITSEC), Orlando, Amerika Birleşik Devletleri, p. 15021. Available at: http://www.ceng.metu.edu.tr/~akyuz/files/blend.pdf (accessed: 05.04.2023).</mixed-citation></citation-alternatives></ref><ref id="cit14"><label>14</label><citation-alternatives><mixed-citation xml:lang="ru">Chen W.-S., Huang M.-L., Wang C.-M. Optimizing color transfer using color similarity measurement // 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS), 2016. Pp. 1–6. DOI: 10.1109/ICIS.2016.7550799</mixed-citation><mixed-citation xml:lang="en">Chen, W.-S., Huang, M.-L., Wang, C.-M. (2016). Optimizing color transfer using color similarity measurement. In: 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS), pp. 1–6. DOI: 10.1109/ICIS.2016.7550799</mixed-citation></citation-alternatives></ref><ref id="cit15"><label>15</label><citation-alternatives><mixed-citation xml:lang="ru">Chang Y., Saito S., Nakajima M. Example-Based color transformation of image and video using basic color categories // IEEE Transactions on Image Processing, 2007. Vol. 16, no. 2. Pp. 329–336. DOI: 10.1109/tip.2006.888347</mixed-citation><mixed-citation xml:lang="en">Chang, Y., Saito, S., Nakajima, M. (2007). Example-Based color transformation of image and video using basic color categories. In: IEEE Transactions on Image Processing, vol. 16, no. 2, pp. 329–336. DOI: 10.1109/tip.2006.888347</mixed-citation></citation-alternatives></ref><ref id="cit16"><label>16</label><citation-alternatives><mixed-citation xml:lang="ru">Xiao X., Ma L. Gradient-Preserving color transfer // Computer Graphics Forum. 2009. Vol. 28, iss. 7. Pp. 1879–1886. DOI: 10.1111/j.1467-8659.2009.01566.x</mixed-citation><mixed-citation xml:lang="en">Xiao, X., Ma, L. (2009). Gradient Preserving color transfer. Computer Graphics Forum, vol. 28, issue 7, pp. 1879–1886. DOI: 10.1111/j.1467-8659.2009.01566.x</mixed-citation></citation-alternatives></ref><ref id="cit17"><label>17</label><citation-alternatives><mixed-citation xml:lang="ru">Oskam T. Fast and stable color balancing for images and augmented reality / T. Oskam, A. Hornung, R.W. Sumner, M. Gross // 2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization &amp; Transmission (3DIMPVT). Zurich, Switzerland, 13–15 October 2012. Pp. 49–56. DOI: 10.1109/3DIMPVT.2012.36</mixed-citation><mixed-citation xml:lang="en">Oskam, T., Hornung, A., Sumner, R.W., Gross, M. (2012). Fast and stable color balancing for images and augmented reality. In: 2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization &amp; Transmission (3DIMPVT), Zurich, Switzerland, October 13–15, pp. 49–56. DOI: 10.1109/3DIMPVT.2012.36</mixed-citation></citation-alternatives></ref><ref id="cit18"><label>18</label><citation-alternatives><mixed-citation xml:lang="ru">Knecht M. Adaptive camera-based color mapping for mixed-reality applications / M. Knecht, C. Traxler, W. Purgathofer, M. Wimmer // 2011 10th IEEE International Symposium on Mixed and Augmented Reality (ISMAR 2011). Switzerland, Basel, 2011. Pp. 165–168. DOI: 10.1109/ISMAR.2011.6092382</mixed-citation><mixed-citation xml:lang="en">Knecht, M., Traxler, C., Purgathofer, W., Wimmer, M. (2011). Adaptive CameraBased Color Mapping For Mixed-Reality Applications. In: 2011 10th IEEE International Symposium on Mixed and Augmented Reality (ISMAR 2011), Basel, Switzerland, pp. 165–168. DOI: 10.1109/ISMAR.2011.6092382</mixed-citation></citation-alternatives></ref><ref id="cit19"><label>19</label><citation-alternatives><mixed-citation xml:lang="ru">Einabadi F., Guillemaut J., Hilton A. Deep neural models for illumination estimation and relighting: A survey // Computer Graphics Forum. 2021. Vol. 40, iss. 6. Pp. 315–331. DOI: 10.1111/cgf.14283</mixed-citation><mixed-citation xml:lang="en">Einabadi, F., Guillemaut, J., Hilton, A. (2021). Deep neural models for illumination estimation and relighting: A survey. Computer Graphics Forum, vol. 40, issue 6, pp. 315–331. DOI: 10.1111/cgf.14283</mixed-citation></citation-alternatives></ref><ref id="cit20"><label>20</label><citation-alternatives><mixed-citation xml:lang="ru">Gardner M., Sunkavalli K., Yumer E. и др. Learning to predict indoor illumination from a single image // ACM Transactions on Graphics. 2017. Vol. 36, iss. 6. Article No.: 176. Pp. 1–14. DOI: 10.1145/3130800.3130891</mixed-citation><mixed-citation xml:lang="en">Gardner, M., Sunkavalli, K., Yumer, E., et al. (2017). Learning to predict indoor illumination from a single image. ACM Transactions on Graphics, vol. 36, issue 6, Article No.: 176, pp. 1–14. DOI: 10.1145/3130800.3130891</mixed-citation></citation-alternatives></ref><ref id="cit21"><label>21</label><citation-alternatives><mixed-citation xml:lang="ru">Song S., Funkhouser T. Neural illumination: Lighting prediction for indoor environments [Электронный ресурс] // Proceedings 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019. Pp. 6918–6926. DOI: 10.48550/arXiv.1906.07370 (дата обращения: 05.04.2023).</mixed-citation><mixed-citation xml:lang="en">Song, S., Funkhouser, T. (2019). Neural illumination: Lighting prediction for indoor environments. In: Proceedings 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6918–6926. DOI: 10.48550/arXiv.1906.07370 (accessed: 05.04.2023).</mixed-citation></citation-alternatives></ref><ref id="cit22"><label>22</label><citation-alternatives><mixed-citation xml:lang="ru">Cheng D. Learning scene illumination by pairwise photos from rear and front mobile cameras / D. Cheng, J. Shi, Y. Chen, X. Deng, X. Zhang // Computer Graphics Forum. 2018. Vol. 37, iss. 7. Pp. 213–221. DOI: 10.1111/cgf.13561</mixed-citation><mixed-citation xml:lang="en">Cheng, D., Shi, J., Chen, Y., Deng, X., Zhang, X. (2018). Learning scene illumination by pairwise photos from rear and front mobile cameras. Computer Graphics Forum, vol. 37, issue 7, pp. 213–221. DOI: 10.1111/cgf.13561</mixed-citation></citation-alternatives></ref><ref id="cit23"><label>23</label><citation-alternatives><mixed-citation xml:lang="ru">Hold-Geoffroy Y. Deep outdoor illumination estimation / Y. Hold-Geoffroy, K. Sunkavalli, S. Hadap, E. Gambaretto, J.-F. Lalonde [Электронный ресурс] // Proceedings 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). USA, Honolulu, HI, 2017. Pp. 2373–2382. DOI: 10.1109/CVPR.2017.255 (дата обращения: 05.04.2023).</mixed-citation><mixed-citation xml:lang="en">Hold-Geoffroy, Y., Sunkavalli, K., Hadap, S., Gambaretto, E., Lalonde, J. (2017). Deep outdoor illumination estimation. In: Proceedings 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, pp. 2373–2382. DOI: 10.1109/CVPR.2017.255 (accessed: 05.04.2023).</mixed-citation></citation-alternatives></ref><ref id="cit24"><label>24</label><citation-alternatives><mixed-citation xml:lang="ru">Zhao Y., Guo T. PointAR: Effcient lighting estimation for mobile augmented reality // 16th European Conference on Computer Vision (ECCV'20), 2020. Pp. 678–693. DOI: 10.48550/arXiv.2004.00006</mixed-citation><mixed-citation xml:lang="en">Zhao, Y., Guo, T. (2020). Point AR: Effcient lighting estimation for mobile augmented reality. In: 16th European Conference on Computer Vision (ECCV'20), pp. 678–693. DOI: 10.48550/arXiv.2004.00006</mixed-citation></citation-alternatives></ref><ref id="cit25"><label>25</label><citation-alternatives><mixed-citation xml:lang="ru">Garon M. Fast spatially-varying in-door lighting estimation / M. Garon, K. Sunkavalli, S. Hadap, N. Carr, J. Lalonde [Электронный ресурс] // Proceedings IEEE Conference on Computer Vision and Pattern Recognition, 2019. Pp. 6908–6917. DOI: 10.48550/arXiv.1906.03799 (дата обращения: 05.04.2023).</mixed-citation><mixed-citation xml:lang="en">Garon, M., Sunkavalli, K., Hadap, S., Carr, N., Lalonde, J. (2019). Fast spatiallyvarying in-door lighting estimation. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition, pp. 6908–6917. DOI: 10.48550/arXiv.1906.03799 (accessed: 05.04.2023).</mixed-citation></citation-alternatives></ref><ref id="cit26"><label>26</label><citation-alternatives><mixed-citation xml:lang="ru">LeGendre C., Ma W., Fyffe G. и др. Deep-light: Learning illumination for unconstrained mobile mixed reality [Электронный ресурс] // Proceedings IEEE Conference on Computer Vision and Pattern Recognition, 2019. Pp. 5918–5928. DOI: 10.48550/arXiv.1904.01175 (дата обращения: 05.04.2023).</mixed-citation><mixed-citation xml:lang="en">LeGendre, C., Ma, W., Fyffe, G., et al. (2019). DeepLight: Learning illumination for unconstrained mobile mixed reality. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition, pp. 5918–5928. DOI: 10.48550/arXiv.1904.01175 (accessed: 05.04.2023).</mixed-citation></citation-alternatives></ref><ref id="cit27"><label>27</label><citation-alternatives><mixed-citation xml:lang="ru">Srinivasan P. Lighthouse: Predicting lighting volumes for spatially-coherent illumination / P. Srinivasan, B. Mildenhall, M. Tancik, J. Barron, R. Tucker, N. Snavely [Электронный ресурс] // Proceedings IEEE Conference on Computer Vision and Pattern Recognition, 2020. Pp. 8080–8089. DOI: 10.48550/arXiv.2003.08367 (дата обращения: 05.04.2023).</mixed-citation><mixed-citation xml:lang="en">Srinivasan, P., Mildenhall, B., Tan‐ cik, M., Barron, J., Tucker, R., Snavely, N. (2020). Lighthouse: Predicting lighting volumes for spatially-coherent illumination. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition, pp. 8080–8089. DOI: 10.48550/arXiv.2003.08367 (accessed: 05.04.2023).</mixed-citation></citation-alternatives></ref><ref id="cit28"><label>28</label><citation-alternatives><mixed-citation xml:lang="ru">Tewari A., Fried O., Thies J. и др. State of the art on neural rendering // Computer Graphics Forum. 2020. Vol. 39, iss. 2. Pp. 701–727. DOI: 10.1111/cgf.14022</mixed-citation><mixed-citation xml:lang="en">Tewari, A., Fried, O., Thies, J., et al. (2020). State of the art on neural rendering. Computer Graphics Forum, vol. 39, issue 2, pp. 701–727. DOI: 10.1111/cgf.14022</mixed-citation></citation-alternatives></ref><ref id="cit29"><label>29</label><citation-alternatives><mixed-citation xml:lang="ru">Goodfellow I., Pouget-Abadie J., Mirza M. и др. Generative adversarial nets // Proceedings of the 27th International Conference on Neural Information Processing Systems, 2014. Vol. 2. Pp. 2672–2680. DOI: 10.5555/2969033.2969125</mixed-citation><mixed-citation xml:lang="en">Goodfellow, I., Pouget-Abadie, J., Mirza, M., et al. (2014). Generative adversarial nets. In: Proceedings of the 27th International Conference on Neural Information Processing Systems, vol. 2, pp. 2672–2680. DOI: 10.5555/2969033.2969125</mixed-citation></citation-alternatives></ref><ref id="cit30"><label>30</label><citation-alternatives><mixed-citation xml:lang="ru">Karras T., Laine S., Aila T. A stylebased generator architecture for generative adversarial networks [Электронный ресурс] // 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). USA, Long Beach, CA, 2019. Pp. 4396–4405. DOI: 10.1109/CVPR.2019.00453 (дата обращения: 05.04.2023).</mixed-citation><mixed-citation xml:lang="en">Karras, T., Laine, S., Aila, T. (2019). A style-based generator architecture for generative adversarial networks. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, pp. 4396–4405. DOI: 10.1109/CVPR.2019.00453 (accessed: 05.04.2023).</mixed-citation></citation-alternatives></ref><ref id="cit31"><label>31</label><citation-alternatives><mixed-citation xml:lang="ru">Bi S. Deep CG2Real: Synthetic-to-real translation via image disentanglement / S. Bi, K. Sunkavalli, F. Perazzi, E. Shechtman, V. Kim, R. Ramamoorthi // 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019. Pp. 2730–2739. DOI: 10.1109/ICCV.2019.00282</mixed-citation><mixed-citation xml:lang="en">Bi, S., Sunkavalli, K., Perazzi, F., Shechtman, E., Kim, V., Ramamoorthi, R. (2019). Deep CG2Real: Synthetic-to-real translation via image disentanglement. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 2730–2739. DOI: 10.1109/ICCV.2019.00282</mixed-citation></citation-alternatives></ref><ref id="cit32"><label>32</label><citation-alternatives><mixed-citation xml:lang="ru">Xu Z. Deep view synthesis from sparse photometric images / Z. Xu, S. Bi, K. Sunkavalli, S. Hadap, H. Su, R. Ramamoorthi [Электронный ресурс] // ACM Transactions on Graphics. 2019. Vol. 38, iss. 412. Article No.: 76. Pp. 1–13. DOI: 10.1145/3306346.3323007 (дата обращения: 05.04.2023).</mixed-citation><mixed-citation xml:lang="en">Xu, Z., Bi, S., Sunkavalli, K., Hadap, S., Su, H., Ramamoorthi, R. (2019). Deep view synthesis from sparse photometric images. ACM Transactions on Graphics, vol. 38, issue 412, Article No.: 76, pp. 1–13. DOI: 10.1145/3306346.3323007 (accessed: 05.04.2023).</mixed-citation></citation-alternatives></ref><ref id="cit33"><label>33</label><citation-alternatives><mixed-citation xml:lang="ru">Park T. Semantic image synthesis with spatially-adaptive normalization / T. Park, M. Liu, T. Wang, J. Zhu [Электронный ресурс] // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019. 19 p. DOI: 10.48550/arXiv.1903.07291 (дата обращения: 05.04.2023).</mixed-citation><mixed-citation xml:lang="en">Park, T., Liu, M., Wang, T., Zhu, J. (2019). Semantic image synthesis with spatiallyadaptive normalization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 19 p. DOI: 10.48550/arXiv.1903.07291 (accessed: 05.04.2023).</mixed-citation></citation-alternatives></ref><ref id="cit34"><label>34</label><citation-alternatives><mixed-citation xml:lang="ru">Li Z. Inverse rendering for complex indoor scenes: Shape, spatially-varying lighting and SVBRDF from a single image / Z. Li, M. Shafiei, R. Ramamoorthi, K. Sunkavalli, M. Chandraker [Электронный ресурс] // Proceedings IEEE Conference on Computer Vision and Pattern Recognition, 2020. Pp. 2475–2484. DOI: 10.48550/arXiv.1905.02722 (дата обращения: 05.04.2023).</mixed-citation><mixed-citation xml:lang="en">Li, Z., Shafiei, M., Ramamoorthi, R., Sunkavalli, K., Chandraker, M. (2020). Inverse rendering for complex indoor scenes: Shape, spatially-varying lighting and SVBRDF from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2475–2484. DOI: 10.48550/arXiv.1905.02722 (accessed: 05.04.2023).</mixed-citation></citation-alternatives></ref><ref id="cit35"><label>35</label><citation-alternatives><mixed-citation xml:lang="ru">Zhan F., Yu Y., Wu R. и др. Bi-level feature alignment for semantic image translation and manipulation [Электронный ресурс] // Proceedings IEEE Conference on Computer Vision and Pattern Recognition, 2022. 18 p. DOI: 10.48550/arXiv.2107.03021 (дата обращения: 05.04.2023).</mixed-citation><mixed-citation xml:lang="en">Zhan, F., Yu, Y., Wu, R., et al. (2022). Bi-level feature alignment for semantic image translation and manipulation. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition, 18 p. DOI: 10.48550/arXiv.2107.03021 (accessed: 05.04.2023).</mixed-citation></citation-alternatives></ref><ref id="cit36"><label>36</label><citation-alternatives><mixed-citation xml:lang="ru">Ghasemi Y. Deep learning-based object detection in augmented reality: A systematic review / Y. Ghasemi, H. Jeong, S. Choi, J. Lee, K. Park [Электронный ресурс] // Computers in Industry. 2022. Vol. 139. ID: 103661. DOI: 10.1016/j.compind.2022.103661 (дата обращения: 05.04.2023).</mixed-citation><mixed-citation xml:lang="en">Ghasemi, Y., Jeong, H., Choi, S., Lee, J., Park, K. (2022). Deep learning-based object detection in augmented reality: A systematic review. Computers in Industry, vol. 139, ID: 103661. DOI: 10.1016/j.compind.2022.103661 (accessed: 05.04.2023).</mixed-citation></citation-alternatives></ref></ref-list><fn-group><fn fn-type="conflict"><p>The authors declare that there are no conflicts of interest present.</p></fn></fn-group></back></article>
