{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T10:56:56Z","timestamp":1761649016265,"version":"build-2065373602"},"reference-count":25,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,4,30]],"date-time":"2022-04-30T00:00:00Z","timestamp":1651276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41971339","2018YFB2100704","2018YFC1407605","AR2107","AR2108","AR2120","2019TDJH103"],"award-info":[{"award-number":["41971339","2018YFB2100704","2018YFC1407605","AR2107","AR2108","AR2120","2019TDJH103"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Research and Development Project of China","award":["41971339","2018YFB2100704","2018YFC1407605","AR2107","AR2108","AR2120","2019TDJH103"],"award-info":[{"award-number":["41971339","2018YFB2100704","2018YFC1407605","AR2107","AR2108","AR2120","2019TDJH103"]}]},{"name":"Basal Research Fund of CASM","award":["41971339","2018YFB2100704","2018YFC1407605","AR2107","AR2108","AR2120","2019TDJH103"],"award-info":[{"award-number":["41971339","2018YFB2100704","2018YFC1407605","AR2107","AR2108","AR2120","2019TDJH103"]}]},{"name":"SDUST Research Fund","award":["41971339","2018YFB2100704","2018YFC1407605","AR2107","AR2108","AR2120","2019TDJH103"],"award-info":[{"award-number":["41971339","2018YFB2100704","2018YFC1407605","AR2107","AR2108","AR2120","2019TDJH103"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Common methods of texture reconstruction first build a visual list for each triangular face, and then select the best image for each triangular face based on the graph-cut method. These methods have problems such as high memory consumption, and difficulties in large-area texture reconstruction. Hence, this paper proposes a parallel method for texture reconstruction in large-scale 3D automatic modeling. First, the hierarchical relationships between the texture reconstruction are calculated in accordance with the adjacency relationships between partitioning cells. Second, building contours are extracted based on the 3D mesh model, the tiles are divided into two categories (occlusion and non-occlusion), and the incorrect occlusion relationship is restored based on the occluded tiles. Then, the graph-cut algorithm is constructed to select the best-view label. Finally, the jagged labels between adjacent labels are smoothed to alleviate the problem of texture seams. Oblique photography data from an area of 10 km2 in Dongying, Shandong were used for validation. The experimental results reveal the following: (i) concerning reconstruction efficiency, the Waechter method can perform texture reconstruction only in a small area, whereas with the proposed method, the size of the reconstruction area is not restricted. The memory consumption is improved by factors of approximately 2\u201313. (ii) Concerning reconstruction results, the Waechter method incorrectly reconstructs the textures of partially occluded regions at the tile edges, while the proposed method can reconstruct the textures correctly. (iii) Compared to the Waechter method, the proposed approach has a 30% lower reduction in the number of texture fragments.<\/jats:p>","DOI":"10.3390\/rs14092160","type":"journal-article","created":{"date-parts":[[2022,5,2]],"date-time":"2022-05-02T07:08:58Z","timestamp":1651475338000},"page":"2160","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Parallel Method for Texture Reconstruction in Large-Scale 3D Automatic Modeling Based on Oblique Photography"],"prefix":"10.3390","volume":"14","author":[{"given":"Fei","family":"Wang","sequence":"first","affiliation":[{"name":"College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongchun","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haolin","family":"Cai","sequence":"additional","affiliation":[{"name":"College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenhu","family":"Qu","sequence":"additional","affiliation":[{"name":"Chinese Academy of Surveying and Mapping, Beijing 100036, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuaizhe","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhendong","family":"Liu","sequence":"additional","affiliation":[{"name":"Chinese Academy of Surveying and Mapping, Beijing 100036, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Allene, C., Pons, J.-P., and Keriven, R. (2008, January 8). Seamless image-based texture atlases using multi-band blending. Proceedings of the 2008 19th International Conference on Pattern Recognition, Tampa, FL, USA.","DOI":"10.1109\/ICPR.2008.4761913"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/2945.965346","article-title":"High-quality texture reconstruction from multiple scans","volume":"7","author":"Bernardini","year":"2001","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1016\/j.cag.2008.05.004","article-title":"Masked photo blending: Mapping dense photographic data set on high-resolution sampled 3D models","volume":"32","author":"Callieri","year":"2008","journal-title":"Comput. Graph."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Chen, Z., Zhou, J., Chen, Y., and Wang, G. (2012, January 16\u201318). 3D Texture Mapping in Multi-view Reconstruction. Proceedings of the International Symposium on Visual Computing, Rethymnon, Greece.","DOI":"10.1007\/978-3-642-33179-4_35"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lempitsky, V., and Ivanov, D. (2007, January 17\u201322). Seamless Mosaicing of Image-Based Texture Maps. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA.","DOI":"10.1109\/CVPR.2007.383078"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Li, S., Xiao, X., Guo, B., and Zhang, L. (2020). A Novel OpenMVS-Based Texture Reconstruction Method Based on the Fully Automatic Plane Segmentation for 3D Mesh Models. Remote Sens., 12.","DOI":"10.3390\/rs12233908"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1023\/B:VISI.0000043756.03810.dd","article-title":"Data Processing Algorithms for Generating Textured 3D Building Facade Meshes from Laser Scans and Camera Images","volume":"61","author":"Frue","year":"2005","journal-title":"Int. J. Comput. Vis."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Waechter, M., Moehrle, N., and Goesele, M. (2014, January 6\u201312). Let There Be Color! Large-Scale Texturing of 3D Reconstructions. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_54"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"152","DOI":"10.18178\/ijmlc.2017.7.5.638","article-title":"An Occlusion Detection Algorithm for 3D Texture Reconstruction of multi-View Images","volume":"7","author":"Li","year":"2017","journal-title":"Int. J. Mach. Learn. Comput."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kim, H.S., Ban, Y.J., and Park, C.J. (2018). A seamless texture color adjustment method for large-scale terrain reconstruction. ACM SIGGRAPH 2018 Posters, Association for Computing Machinery.","DOI":"10.1145\/3230744.3230788"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.gmod.2012.09.002","article-title":"Octree-based fusion for realtime 3D reconstruction","volume":"75","author":"Zeng","year":"2013","journal-title":"Graph. Model."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Mostegel, C., Prettenthaler, R., Fraundorfer, F., and Bischof, H. (2017, January 21\u201326). Scalable Surface Reconstruction from Point Clouds with Extreme Scale and Density Diversity. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.268"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Liu, J., Mills, S., and Mccane, B. (2020, January 25\u201328). RocNet: Recursive Octree Network for Efficient 3D Deep Representation. Proceedings of the 2020 International Conference on 3D Vision (3DV), Fukuoka, Japan.","DOI":"10.1109\/3DV50981.2020.00051"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"323","DOI":"10.2174\/1874129001408010323","article-title":"Research on 3D modeling method based on hybrid octree structure","volume":"8","author":"Yujian","year":"2014","journal-title":"Open Electr. Electron. Eng. J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s42492-019-0020-y","article-title":"Scalable point cloud meshing for image-based large-scale 3D modeling","volume":"2","author":"Han","year":"2019","journal-title":"Vis. Comput. Ind. Biomed. Art"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, F., Liu, Z., Zhu, H., and Wu, P. (2021). A Parallel Method for Open Hole Filling in Large-Scale 3D Automatic Modeling Based on Oblique Photography. Remote Sens., 13.","DOI":"10.3390\/rs13173512"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"31875","DOI":"10.1007\/s11042-018-6193-0","article-title":"Single image 3D reconstruction based on control point grid","volume":"77","author":"Zhang","year":"2018","journal-title":"Multimed. Tools Appl."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhu, L., Shen, S., Gao, X., and Hu, Z. (2020). Urban Scene Vectorized Modeling Based on Contour Deformation. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9030162"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bakshi, N., Shivani, S., Tiwari, S., and Khurana, M. (2021). Optimized Z-Buffer Using Divide and Conquer. Innovations in Computational Intelligence and Computer Vision, Springer.","DOI":"10.1007\/978-981-15-6067-5_6"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Greene, N., Kass, M., and Miller, G. (1993, January 2\u20136). Hierarchical Z-buffer visibility. Proceedings of the 20th Annual Conference on Computer Graphics And Interactive Techniques, Anaheim, CA, USA.","DOI":"10.1145\/166117.166147"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1222","DOI":"10.1109\/34.969114","article-title":"Fast approximate energy minimization via graph cuts","volume":"23","author":"Boykov","year":"2001","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_22","unstructured":"Schmidt, M., and Alahari, K. (2011). Generalized fast approximate energy minimization via graph cuts: Alpha-expansion beta-shrink moves. arXiv."},{"key":"ref_23","first-page":"338","article-title":"Automatic texture optimization for 3D urban reconstruction","volume":"46","author":"Ming","year":"2017","journal-title":"Acta Geod. Et Cartogr. Sin."},{"key":"ref_24","unstructured":"Ringbeck, T., and Hagebeuker, B. (2008). A Performance Review of 3D TOF Vision Systems in Comparison to Stereo Vision Systems. Stereo Vision, I-Tech."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zureiki, A., Devy, M., and Chatila, R. (2008). Stereo matching and graph cuts. Stereo Vision, I-Tech.","DOI":"10.5772\/5888"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/9\/2160\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:05:03Z","timestamp":1760137503000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/9\/2160"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,30]]},"references-count":25,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["rs14092160"],"URL":"https:\/\/doi.org\/10.3390\/rs14092160","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,4,30]]}}}