The method of variable scale mesh primitive extraction for monomeric and structural reconstruction of buildings

doi:10.11947/j.AGCS.2023.20220275

Abstract

Abstract: The existing region-growing algorithm extracts planar primitives inaccurately and irregularly, ignoring the geometric structures with smaller scales. The method of variable scale mesh primitive extraction for monomeric and structural reconstruction of buildings is proposed in this paper, which uses a multi-scale region-growing algorithm to extract the corresponding planar primitives from the geometric structures of different scale sizes of the mesh, improving the accuracy and completeness of planar primitive extraction. The topological quality of planar primitives is further improved by topology optimization of planar primitives, and the reconstruction accuracy of polygonal surface model is enhanced. And the area-priority strategy is used to improve the merging efficiency of co-planar primitives. The experimental results show that this method can extract the planar primitives more accurately, preserve the small-scale geometry of the buildings, and generate a more compact and structural polygonal surface model of the buildings.

Key words: monomeric and structural reconstruction, multi-scale region-growing, topology optimization, polygonal surface model

CLC Number:

P237

LIU Haibing, QU Yingjie, YAN Qingsong, DENG Fei. The method of variable scale mesh primitive extraction for monomeric and structural reconstruction of buildings[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(11): 1953-1961.

References

[1] HERBERT G, CHEN Xuwei. A comparison of usefulness of 2D and 3D representations of urban planning[J]. Cartography and Geographic Information Science, 2015, 42(1):22-32.
[2] YAO Zhihang, NAGEL C, KUNDE F, et al. 3DcityDB:a 3D geodatabase solution for the management, analysis, and visualization of semantic 3D city models based on CityGML[J]. Open Geospatial Data, Software and Standards, 2018, 3(1):1-26.
[3] STOTER J, PETERS R, COMMANDEUR T, et al. Automated reconstruction of 3D input data for noise simulation[J]. Computers, Environment and Urban Systems, 2020, 80:101424.
[4] CAPPELLE C, EL NAJJAR M E, CHARPILLET F, et al. Virtual 3D city model for navigation in urban areas[J]. Journal of Intelligent and Robotic Systems, 2012, 66(3):377-399.
[5] KARGAS A, LOUMOS G, VAROUTAS D. Using different ways of 3D reconstruction of historical cities for gaming purposes:the case study of Nafplio[J]. Heritage, 2019, 2(3):1799-1811.
[6] LI Minglei, WONKA P, NAN Liangliang. Manhattan-world urban reconstruction from point clouds[C]//Proceedings of 2016 European Conference on Computer Vision. Cham:Springer, 2016:54-69.
[7] SCHNABEL R, WAHL R, KLEIN R. Efficient RANSAC for point-cloud shape detection[J]. Computer Graphics Forum, 2007, 26(2):214-226.
[8] NAN L L, WONKA P. PolyFit:polygonal surface reconstruction from point clouds[C]//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice:IEEE, 2017:2372-2380.
[9] CHEN Z, LEDOUX H, KHADEMI S, et al. Reconstructing compact building models from point clouds using deep implicit fields[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2022, 194:58-73.
[10] ERLER P, GUERRERO P, OHRHALLINGER S, et al. Points2Surf:learning implicit surfaces from point clouds[EB/OL].[2022-06-28]. https://arxiv.org/pdf/2007.10453.pdf.
[11] XIE Linfu, HU Han, ZHU Qing, et al. Combined rule-based and hypothesis-based method for building model reconstruction from photogrammetric point clouds[J]. Remote Sensing, 2021, 13(6):1107.
[12] FANG H, LAFARGE F. Connect-and-slice:an hybrid approach for reconstructing 3D objects[C]//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle:IEEE, 2020:13487-13495.
[13] 崔扬. 移动LiDAR点云室内三维结构化重建方法和关键技术研究[J]. 测绘学报, 2021, 50(7):990. DOI:10.11947/j.AGCS.2021.20200592. CUI Yang. Research on methodology and the key technology of indoor 3D structured reconstruction from mobile LiDAR point clouds[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(7):990. DOI:10.11947/j.AGCS.2021.20200592.
[14] ZHU Lingjie, SHEN Shuhan, HU Lihua, et al. Variational building modeling from urban MVS meshes[C]//Proceedings of 2017 International Conference on 3D Vision. Qingdao:IEEE, 2018:318-326.
[15] COHEN-STEINER D, ALLIEZ P, DESBRUN M. Variational shape approximation[J]. ACM Transactions on Graphics, 23(3):905-914.
[16] CityGML. CityGML[EB/OL].[2023-06-28].http://www.opengeospatial.org/standards/citygml.
[17] ZHU Lingjie, SHEN Shuhan, GAO Xiang, et al. Large scale urban scene modeling from MVS meshes[C]//Proceedings of the 15th European Conference. Munich:Springer, 2018:640-655.
[18] BOYKOV Y, KOLMOGOROV V. An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(9):1124-1137.
[19] BOYKOV Y, VEKSLER O, ZABIH R. Fast approximate energy minimization via graph cuts[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(11):1222-1239.
[20] ZHU Lingjie, SHEN Shuhan, GAO Xiang, et al. Urban scene vectorized modeling based on contour deformation[J]. ISPRS International Journal of Geo-Information, 2020, 9(3):162.
[21] BOUZAS V, LEDOUX H, NAN L. Structure-aware building mesh polygonization[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 167:432-442.
[22] YAN Li, LI Yao, XIE Hong. Urban building mesh polygonization based on 1-ring patch and topology optimization[J]. Remote Sensing, 2021, 13(23):4777.
[23] GATZKE T D, GRIMM C M. Estimating curvature on triangular meshes[J]. International Journal of Shape Modeling, 2006, 12(1):1-28.
[24] PAULY M, GROSS M, KOBBELT L P. Efficient simplification of point-sampled surfaces[C]//Proceedings of 2002 IEEE Visualization. Boston:IEEE, 2022:163-170.
[25] OTSU N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1):62-66.
[26] 邓非, 颜青松, 肖腾. 多视密集匹配并行传播GPU-PatchMatch算法[J]. 测绘学报, 2020, 49(2):181-190. DOI:10.11947/j.AGCS.2020.20180459. DENG Fei, YAN Qingsong, XIAO Teng. A GPU-PatchMatch multi-view dense matching algorithm based on parallel propagation[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(2):181-190. DOI:10.11947/j.AGCS.2020.20180459.
[27] GARLAND M, HECKBERT P S. Surface simplification using quadric error metrics[C]//Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques.New York:ACM Press, 1997:209-216.
[28] GUTHE M, BORODIN P, KLEIN R. Fast and accurate Hausdorff distance calculation between meshes[EB/OL].[2022-06-28].http://wscg.zcu.cz/wscg2005/Papers_2005/Full/F61-full.pdf.