建筑物单体结构化重建的变尺度网格基元提取法

doi:10.11947/j.AGCS.2023.20220275

测绘学报 ›› 2023, Vol. 52 ›› Issue (11): 1953-1961.doi: 10.11947/j.AGCS.2023.20220275

建筑物单体结构化重建的变尺度网格基元提取法

刘海兵, 曲英杰, 颜青松, 邓非

武汉大学测绘学院, 湖北武汉 430079

收稿日期:2022-04-06 修回日期:2022-10-01 发布日期:2023-12-15
通讯作者: 邓非 E-mail:fdeng@sgg.whu.edu.cn
作者简介:刘海兵(1998-),男,博士生,研究方向为三维重建。E-mail:liuhb_whu@whu.edu.cn
基金资助:
装发快速支持项目(80911010202)

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

LIU Haibing, QU Yingjie, YAN Qingsong, DENG Fei

School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China

Received:2022-04-06 Revised:2022-10-01 Published:2023-12-15
Supported by:
Quick Support Program (No. 80911010202)

摘要/Abstract

摘要： 现有的区域生长算法提取平面基元不准确不规则,忽略了尺度较小的几何结构。本文提出了一种建筑物单体结构化重建的变尺度网格基元提取方法,采用多尺度区域生长算法从网格不同尺度大小的几何结构中提取出对应的平面基元,提升了平面基元提取的准确性和完整性。通过平面基元拓扑优化进一步改善平面基元的拓扑质量,提升多边形表面模型的重建精度;并采用面积优先级策略提高共面平面基元合并效率。试验结果表明,本文方法能够更准确地进行平面基元的提取,保留了建筑物较小尺度的几何结构,生成了更简洁紧凑、结构化的建筑物多边形表面模型。

关键词: 单体结构化重建, 多尺度区域生长, 拓扑优化, 多边形表面模型

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

中图分类号:

P237

刘海兵, 曲英杰, 颜青松, 邓非. 建筑物单体结构化重建的变尺度网格基元提取法[J]. 测绘学报, 2023, 52(11): 1953-1961.

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.

参考文献

[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.

建筑物单体结构化重建的变尺度网格基元提取法

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

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