乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T09:21:01Z","timestamp":1724577661404},"reference-count":84,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2020,9,23]],"date-time":"2020-09-23T00:00:00Z","timestamp":1600819200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002261","name":"Russian Foundation for Basic Research","doi-asserted-by":"publisher","award":["17-29-04410, 17-29-04509"],"id":[{"id":"10.13039\/501100002261","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"The latest advances in technical characteristics of unmanned aerial systems (UAS) and their onboard sensors opened the way for smart flying vehicles exploiting new application areas and allowing to perform missions seemed to be impossible before. One of these complicated tasks is the 3D reconstruction and monitoring of large-size, complex, grid-like structures as radio or television towers. Although image-based 3D survey contains a lot of visual and geometrical information useful for making preliminary conclusions on construction health, standard photogrammetric processing fails to perform dense and robust 3D reconstruction of complex large-size mesh structures. The main problem of such objects is repeated and self-occlusive similar elements resulting in false feature matching. This paper presents a method developed for an accurate Multi-View Stereo (MVS) dense 3D reconstruction of the Shukhov Radio Tower in Moscow (Russia) based on UAS photogrammetric survey. A key element for the successful image-based 3D reconstruction is the developed WireNetV2 neural network model for robust automatic semantic segmentation of wire structures. The proposed neural network provides high matching quality due to an accurate masking of the tower elements. The main contributions of the paper are: (1) a deep learning WireNetV2 convolutional neural network model that outperforms the state-of-the-art results of semantic segmentation on a dataset containing images of grid structures of complicated topology with repeated elements, holes, self-occlusions, thus providing robust grid structure masking and, as a result, accurate 3D reconstruction, (2) an advanced image-based pipeline aided by a neural network for the accurate 3D reconstruction of the large-size and complex grid structured, evaluated on UAS imagery of Shukhov radio tower in Moscow.<\/jats:p>","DOI":"10.3390\/rs12193128","type":"journal-article","created":{"date-parts":[[2020,9,24]],"date-time":"2020-09-24T06:56:43Z","timestamp":1600930603000},"page":"3128","source":"Crossref","is-referenced-by-count":15,"title":["3D Reconstruction of a Complex Grid Structure Combining UAS Images and Deep Learning"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-4466-244X","authenticated-orcid":false,"given":"Vladimir A.","family":"Knyaz","sequence":"first","affiliation":[{"name":"Moscow Institute of Physics and Technology (MIPT), 141701 Dolgoprudy, Russia"},{"name":"State Research Institute of Aviation Systems (GosNIIAS), 125319 Moscow, Russia"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-2912-9986","authenticated-orcid":false,"given":"Vladimir V.","family":"Kniaz","sequence":"additional","affiliation":[{"name":"Moscow Institute of Physics and Technology (MIPT), 141701 Dolgoprudy, Russia"},{"name":"State Research Institute of Aviation Systems (GosNIIAS), 125319 Moscow, Russia"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-6097-5342","authenticated-orcid":false,"given":"Fabio","family":"Remondino","sequence":"additional","affiliation":[{"name":"Bruno Kessler Foundation (FBK), 38123 Trento, Italy"}]},{"given":"Sergey Y.","family":"Zheltov","sequence":"additional","affiliation":[{"name":"Moscow Institute of Physics and Technology (MIPT), 141701 Dolgoprudy, Russia"},{"name":"State Research Institute of Aviation Systems (GosNIIAS), 125319 Moscow, Russia"}]},{"given":"Armin","family":"Gruen","sequence":"additional","affiliation":[{"name":"ETH Zurich, 8092 Zurich, Switzerland"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.isprsjprs.2014.02.013","article-title":"Unmanned aerial systems for photogrammetry and remote sensing: A review","volume":"92","author":"Colomina","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12518-013-0120-x","article-title":"UAV for 3D mapping applications: A review","volume":"6","author":"Nex","year":"2014","journal-title":"Appl. Geomat."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1111\/phor.12244","article-title":"RPV, UAV, UAS, RPAS \u2026 or just drone?","volume":"33","author":"Granshaw","year":"2018","journal-title":"Photogramm. Rec."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.paerosci.2017.04.003","article-title":"Classifications, applications, and design challenges of drones: A review","volume":"91","author":"Hassanalian","year":"2017","journal-title":"Prog. Aerosp. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.5194\/nhess-18-1079-2018","article-title":"Review article: The use of remotely piloted aircraft systems (RPASs) for natural hazards monitoring and management","volume":"18","author":"Giordan","year":"2018","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/j.culher.2014.09.014","article-title":"Laser scanning and 3D modeling of the Shukhov hyperboloid tower in Moscow","volume":"16","author":"Leonov","year":"2015","journal-title":"J. Cult. Herit."},{"key":"ref_7","first-page":"331","article-title":"Open-source image-based 3d reconstruction pipelines: Review, comparison and evaluation","volume":"XLII-2\/W17","author":"Stathopoulou","year":"2019","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Mandlburger, G., Pfennigbauer, M., Schwarz, R., Flory, S., and Nussbaumer, L. (2020). Concept and Performance Evaluation of a Novel UAV-Borne Topo-Bathymetric LiDAR Sensor. Remote Sens., 12.","DOI":"10.3390\/rs12060986"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Anthony, D., Elbaum, S., Lorenz, A., and Detweiler, C. (2014, January 14\u201318). On crop height estimation with UAVs. Proceedings of the 2014 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Chicago, IL, USA.","DOI":"10.1109\/IROS.2014.6943245"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4026","DOI":"10.3390\/rs70404026","article-title":"Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images","volume":"7","author":"Candiago","year":"2015","journal-title":"Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kameyama, S., and Sugiura, K. (2019). Estimating Tree Height and Volume Using Unmanned Aerial Vehicle Photography and SfM Technology, with Verification of Result Accuracy. Drones, 3.","DOI":"10.3390\/drones4020019"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Nex, F., and Remondino, F. (2019). Preface: Latest Developments, Methodologies, and Applications Based on UAV Platforms. Drones, 3.","DOI":"10.3390\/drones3010026"},{"key":"ref_13","first-page":"583","article-title":"Archaeological site monitoring: UAV photogrammetry can be an answer","volume":"39","author":"Rinaudo","year":"2012","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_14","first-page":"508","article-title":"Multisource data fusion for documenting archaeological sites","volume":"Volume 10427","author":"Bruzzone","year":"2017","journal-title":"Image and Signal Processing for Remote Sensing XXIII"},{"key":"ref_15","first-page":"526","article-title":"UAVs For The Documentation Of Archaeological Excavations","volume":"38","author":"Sauerbier","year":"2010","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"107148","DOI":"10.1016\/j.comnet.2020.107148","article-title":"A compilation of UAV applications for precision agriculture","volume":"172","author":"Sarigiannidis","year":"2020","journal-title":"Comput. Netw."},{"key":"ref_17","first-page":"1207","article-title":"The Photogrammetric Potential of Low-Cost UAVS in Forestry and Agriculture","volume":"31","author":"Grenzdorffer","year":"2008","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Hildmann, H., and Kovacs, E. (2019). Review: Using Unmanned Aerial Vehicles (UAVs) as Mobile Sensing Platforms (MSPs) for Disaster Response, Civil Security and Public Safety. Drones, 3.","DOI":"10.3390\/drones3030059"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Gonzalez, L., Montes, G., Puig, E., Johnson, S., Mengersen, K., and Gaston, K. (2016). Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence Revolutionizing Wildlife Monitoring and Conservation. Sensors, 16.","DOI":"10.3390\/s16010097"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Jakovljevic, G., Govedarica, M., and Alvarez-Taboada, F. (2020). A Deep Learning Model for Automatic Plastic Mapping Using Unmanned Aerial Vehicle (UAV) Data. Remote Sens., 12.","DOI":"10.3390\/rs12091515"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ya\u2019acob, N., Zolkapli, M., Johari, J., Yusof, A.L., Sarnin, S.S., and Asmadinar, A.Z. (2017, January 9\u201310). UAV environment monitoring system. Proceedings of the 2017 International Conference on Electrical, Electronics and System Engineering (ICEESE), Kanazawa, Japan.","DOI":"10.1109\/ICEESE.2017.8298395"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Iglesias, L., De Santos-Berbel, C., Pascual, V., and Castro, M. (2019). Using Small Unmanned Aerial Vehicle in 3D Modeling of Highways with Tree-Covered Roadsides to Estimate Sight Distance. Remote Sens., 11.","DOI":"10.3390\/rs11222625"},{"key":"ref_23","first-page":"515","article-title":"Photogrammetric techniques for road surface analysis","volume":"41","author":"Knyaz","year":"2016","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Romero-Chambi, E., Villarroel-Quezada, S., Atencio, E., and Munoz-La Rivera, F. (2020). Analysis of Optimal Flight Parameters of Unmanned Aerial Vehicles (UAVs) for Detecting Potholes in Pavements. Appl. Sci., 10.","DOI":"10.3390\/app10124157"},{"key":"ref_25","first-page":"183","article-title":"Three-dimensional building reconstruction using images obtained by unmanned aerial vehicles","volume":"38","author":"Wefelscheid","year":"2011","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_26","unstructured":"Qin, R., Gr\u00fcn, A., and Huang, X. (2012, January 26\u201330). UAV project\u2014Building a reality-based 3D model of the NUS (National University of Singapore) campus. Proceedings of the 33rd Asian Conference on Remote Sensing, Pattaya, Thailand."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Cal\u00ec, M., and Ambu, R. (2018). Advanced 3D Photogrammetric Surface Reconstruction of Extensive Objects by UAV Camera Image Acquisition. Sensors, 18.","DOI":"10.3390\/s18092815"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hein, D., Kraft, T., Brauchle, J., and Berger, R. (2019). Integrated UAV-Based Real-Time Mapping for Security Applications. ISPRS Int. J. Geo Inf., 8.","DOI":"10.3390\/ijgi8050219"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3072959.2990496","article-title":"Image-Based Reconstruction of Wire Art","volume":"36","author":"Liu","year":"2017","journal-title":"ACM Trans. Graph."},{"key":"ref_30","unstructured":"Hofer, M., Wendel, A., and Bischof, H. (2013, January 16\u201318). Line-based 3D Reconstruction of Wiry Objects. Proceedings of the 18th Computer Vision Winter Workshop, Petersburg, Russia."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Bacharidis, K., Sarri, F., and Ragia, L. (2020). 3D Building Fa\u00e7ade Reconstruction Using Deep Learning. ISPRS Int. J. Geo Inf., 9.","DOI":"10.3390\/ijgi9050322"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Martin, T., Montes, J., Bazin, J.C., and Popa, T. (2014). Topology-Aware Reconstruction of Thin Tubular Structures. SIGGRAPH Asia 2014 Technical Briefs, Association for Computing Machinery.","DOI":"10.1145\/2669024.2669035"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"65-1","DOI":"10.1145\/2461912.2461913","article-title":"L1-Medial Skeleton of Point Cloud","volume":"32","author":"Huang","year":"2013","journal-title":"ACM Trans. Graph."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Bebis, G., Boyle, R., Parvin, B., Koracin, D., Li, B., Porikli, F., Zordan, V., Klosowski, J., Coquillart, S., and Luo, X. (2013). Reconstruction of Wire Structures from Scanned Point Clouds. Advances in Visual Computing, Springer.","DOI":"10.1007\/978-3-642-41939-3"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"20180193","DOI":"10.1098\/rsif.2018.0193","article-title":"Imaging and analysis of a three-dimensional spider web architecture","volume":"15","author":"Su","year":"2018","journal-title":"J. R. Soc. Interface"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Nooruddin, M., and Rahman, M. (2018, January 13\u201315). Improved 3D Reconstruction for Images having Moving Object using Semantic Image Segmentation and Binary Masking. Proceedings of the 2018 4th International Conference on Electrical Engineering and Information Communication Technology (iCEEiCT), Dhaka, Bangladesh.","DOI":"10.1109\/CEEICT.2018.8628064"},{"key":"ref_37","first-page":"175","article-title":"Segmentation of image pairs for 3d reconstruction","volume":"XLII-2\/W16","author":"Mohammed","year":"2019","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_38","first-page":"98","article-title":"Automatic masking for robust 3D-2D image registration in image-guided spine surgery","volume":"Volume 9786","author":"Webster","year":"2016","journal-title":"Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Kaneko, M., Iwami, K., Ogawa, T., Yamasaki, T., and Aizawa, K. (2018, January 18\u201322). Mask-SLAM: Robust Feature-Based Monocular SLAM by Masking Using Semantic Segmentation. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00063"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wan, Q., Li, Y., Cui, H., and Feng, Z. (2019, January 28\u201330). 3D-Mask-GAN:Unsupervised Single-View 3D Object Reconstruction. Proceedings of the 2019 6th International Conference on Behavioral, Economic and Socio-Cultural Computing (BESC), Beijing, China.","DOI":"10.1109\/BESC48373.2019.8963264"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Leibe, B., Matas, J., Sebe, N., and Welling, M. (2016). Learning a Predictable and Generative Vector Representation for Objects. Computer Vision\u2014ECCV 2016, Springer International Publishing.","DOI":"10.1007\/978-3-319-46454-1"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Shin, D., Fowlkes, C.C., and Hoiem, D. (2018, January 18\u201322). Pixels, Voxels, and Views: A Study of Shape Representations for Single View 3D Object Shape Prediction. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00323"},{"key":"ref_43","unstructured":"Leibe, B., Matas, J., Sebe, N., and Welling, M. (2016). 3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction. Computer Vision\u2014ECCV 2016, Springer International Publishing."},{"key":"ref_44","unstructured":"Xie, H., Yao, H., Sun, X., Zhou, S., and Zhang, S. (November, January 27). Pix2Vox: Context-Aware 3D Reconstruction From Single and Multi-View Images. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Seoul, Korea."},{"key":"ref_45","unstructured":"Shin, D., Ren, Z., Sudderth, E.B., and Fowlkes, C.C. (November, January 27). 3D Scene Reconstruction With Multi-Layer Depth and Epipolar Transformers. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Seoul, Korea."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Knyaz, V.A., Kniaz, V.V., and Remondino, F. (2018). Image-to-Voxel Model Translation with Conditional Adversarial Networks. Computer Vision\u2014ECCV 2018, Springer.","DOI":"10.1007\/978-3-030-11009-3_37"},{"key":"ref_47","first-page":"403","article-title":"Generative adversarial networks for single photo 3D reconstruction","volume":"XLII-2\/W9","author":"Kniaz","year":"2019","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_48","unstructured":"Yi, K.M., Trulls, E., Lepetit, V., and Fua, P. (2018). LIFT: Learned Invariant Feature Transform. Computer Vision\u2014ECCV 2018, Springer."},{"key":"ref_49","unstructured":"Ono, Y., Trulls, E., Fua, P., and Yi, K.M. (2018, January 3\u20138). LF-Net: Learning Local Features from Images. Proceedings of the Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, Montr\u00e9al, QC, Canada."},{"key":"ref_50","unstructured":"Christiansen, P.H., Kragh, M.F., Brodskiy, Y., and Karstoft, H. (2019). UnsuperPoint: End-to-end Unsupervised Interest Point Detector and Descriptor. arXiv."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Shen, X., Wang, C., Li, X., Yu, Z., Li, J., Wen, C., Cheng, M., and He, Z. (2019). RF-Net: An End-to-End Image Matching Network based on Receptive Field. arXiv.","DOI":"10.1109\/CVPR.2019.00832"},{"key":"ref_52","first-page":"59","article-title":"GANcoder: Robust feature point matching using conditional adversarial auto-encoder","volume":"Volume 11353","author":"Schelkens","year":"2020","journal-title":"Optics, Photonics and Digital Technologies for Imaging Applications VI"},{"key":"ref_53","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2019, January 13\u201317). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical image computing and Computer-Assisted Intervention, Shenzhen, China."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A.G., Zhu, M., Zhmoginov, A., and Chen, L. (2018, January 18\u201322). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Minaee, S., Boykov, Y., Porikli, F.M., Plaza, A.J., Kehtarnavaz, N., and Terzopoulos, D. (2020). Image Segmentation Using Deep Learning: A Survey. arXiv.","DOI":"10.1109\/TPAMI.2021.3059968"},{"key":"ref_56","first-page":"259","article-title":"Conditional GANs for semantic segmentation of multispectral satellite images","volume":"Volume 10789","author":"Bruzzone","year":"2018","journal-title":"Image and Signal Processing for Remote Sensing XXIV"},{"key":"ref_57","first-page":"283","article-title":"Deep learning for dense labeling of hydrographic regions in very high resolution imagery","volume":"Volume 11155","author":"Bruzzone","year":"2019","journal-title":"Image and Signal Processing for Remote Sensing XXV"},{"key":"ref_58","unstructured":"Shelhamer, E., Long, J., and Darrell, T. (2016). Fully Convolutional Networks for Semantic Segmentation. arXiv."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Christiansen, P., Nielsen, L.N., Steen, K.A., J\u00f8rgensen, R.N., and Karstoft, H. (2016). DeepAnomaly: Combining Background Subtraction and Deep Learning for Detecting Obstacles and Anomalies in an Agricultural Field. Sensors, 16.","DOI":"10.3390\/s16111904"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Huang, P., Matzen, K., Kopf, J., Ahuja, N., and Huang, J. (2018, January 18\u201322). DeepMVS: Learning Multi-view Stereopsis. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00298"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Kuhn, A., Sormann, C., Rossi, M., Erdler, O., and Fraundorfer, F. (2019). DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. arXiv.","DOI":"10.1109\/3DV50981.2020.00050"},{"key":"ref_62","first-page":"1135","article-title":"Multi-view stereo with semantic priors","volume":"XLII-2\/W15","author":"Stathopoulou","year":"2019","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Wei, Z., Wang, Y., Yi, H., Chen, Y., and Wang, G. (2020). Semantic 3D Reconstruction with Learning MVS and 2D Segmentation of Aerial Images. Appl. Sci., 10.","DOI":"10.3390\/app10041275"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"64","DOI":"10.21014\/acta_imeko.v7i3.592","article-title":"A software tool for the semi-automatic segmentation of architectural 3D models with semantic annotation and Web fruition","volume":"7","year":"2018","journal-title":"ACTA IMEKO"},{"key":"ref_65","unstructured":"Stathopoulou, E.K., and Remondino, F. (2020). Multi view stereo with semantic priors. arXiv."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Roberts, R., Sinha, S.N., Szeliski, R., and Steedly, D. (2011). Structure from motion for scenes with large duplicate structures. CVPR 2011, IEEE.","DOI":"10.1109\/CVPR.2011.5995549"},{"key":"ref_67","unstructured":"Jiang, N., Tan, P., and Cheong, L.F. (2012, January 6\u201321). Seeing double without confusion: Structure-from-motion in highly ambiguous scenes. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA."},{"key":"ref_68","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A.C., and Bengio, Y. (2014, January 8\u201313). Generative Adversarial Nets. Proceedings of the Annual Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-to-Image Translation with Conditional Adversarial Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_70","unstructured":"Luc, P., Couprie, C., Chintala, S., and Verbeek, J. (2016). Semantic Segmentation using Adversarial Networks. arXiv."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Xiao, T., Liu, Y., Zhou, B., Jiang, Y., and Sun, J. (2018). Unified Perceptual Parsing for Scene Understanding. Computer Vision\u2014ECCV 2018, Springer.","DOI":"10.1007\/978-3-030-01228-1_26"},{"key":"ref_72","unstructured":"Sun, K., Zhao, Y., Jiang, B., Cheng, T., Xiao, B., Liu, D., Mu, Y., Wang, X., Liu, W., and Wang, J. (2019). High-Resolution Representations for Labeling Pixels and Regions. arXiv."},{"key":"ref_73","first-page":"435","article-title":"Wire structure image-based 3D reconstruction aided by deep learning","volume":"43","author":"Kniaz","year":"2020","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1947","DOI":"10.1109\/TPAMI.2018.2856256","article-title":"StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks","volume":"41","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_75","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019, January 3). PyTorch: An Imperative Style, High-Performance Deep Learning Library. Proceedings of the Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, Montr\u00e9al, QC, Canada."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Leibe, B., Matas, J., Sebe, N., and Welling, M. (2016). Pixelwise View Selection for Unstructured Multi-View Stereo. Computer Vision\u2014ECCV 2016, Springer International Publishing.","DOI":"10.1007\/978-3-319-46448-0"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Sch\u00f6nberger, J.L., and Frahm, J. (2016, January 27\u201330). Structure-from-Motion Revisited. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.445"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"103:1","DOI":"10.1145\/3197517.3201286","article-title":"Full 3D reconstruction of transparent objects","volume":"37","author":"Wu","year":"2018","journal-title":"ACM Trans. Graph."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Bianco, S., Ciocca, G., and Marelli, D. (2018). Evaluating the Performance of Structure from Motion Pipelines. J. Imaging, 4.","DOI":"10.3390\/jimaging4080098"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1145\/1409060.1409085","article-title":"Time-resolved 3d capture of non-stationary gas flows","volume":"27","author":"Atcheson","year":"2008","journal-title":"ACM Trans. Graph."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Ji, Y., Ye, J., and Yu, J. (2013, January 23\u201328). Reconstructing Gas Flows Using Light-Path Approximation. Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.324"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Ihrke, I., and Magnor, M.A. (2004). Image-based tomographic reconstruction of flames. ACM SIGGRAPH 2004 Sketches (SIGGRAPH\u201904), Association for Computing Machinery.","DOI":"10.1145\/1186223.1186243"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1007\/s00371-014-0987-5","article-title":"Reconstruction of three-dimensional flame with color temperature","volume":"31","author":"Wu","year":"2015","journal-title":"Vis. Comput."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.cviu.2016.03.017","article-title":"Efficient 3D scene abstraction using line segments","volume":"157","author":"Hofer","year":"2017","journal-title":"Comput. Vis. 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