Realize building monomerization of 3D model based on deep learning

doi:10.13474/j.cnki.11-2246.2022.0350

Abstract

Abstract: The oblique photography 3D modeling technology can quickly obtain the real 3D model of the target area, but the model under this mechanism is a continuous triangulated irregular network(TIN), which makes it impossible to analyze specific geographic objects. In this paper, two methods of object-oriented image analysis and deep learning semantic segmentation are used to extract buildings and comparative analysis. The result shows that semantic segmentation method can effectively extract the buildings in the digital orthophotos corresponding to the tilt model. By post-processing the extraction results and giving the vector data the basic attributes of the building, the vector datas are used to realize the building monomerization and publish 3D models and data services, which can more effectively use the oblique photography model to show the geographical space and the building information.

Key words: oblique photography, 3D modeling, image segmentation, deep learning, model monomerization

CLC Number:

WANG Wenna, ZHANG Gong, WU Kan, WANG Rui, QI Lizhuang. Realize building monomerization of 3D model based on deep learning[J]. Bulletin of Surveying and Mapping, 2022, 0(12): 14-18,23.

References

[1] 周晓敏, 孟晓林, 张雪萍, 等.倾斜摄影测量的城市真三维模型构建方法[J].测绘科学, 2016, 41(9):159-163.
[2] 杨国东, 王民水.倾斜摄影测量技术应用及展望[J].测绘与空间地理信息, 2016, 39(1):13-15.
[3] AICARDI I, CHIABRANDO F, GRASSO N, et al. Uav photogrammetry with oblique images:first analysis on data acquisition and processing[J]. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, XLI-B1:835-842.
[4] 张红华, 赵威成, 刘强凯. 倾斜摄影实景三维模型建筑物单体化方法研究[J]. 北京测绘, 2020, 34(3):289-291.
[5] 聂赞, 文琳, 黄山, 等. 倾斜摄影的单体化建模研究[J]. 地理空间信息, 2019, 17(3):113-114, 122.
[6] 陈思, 冯学兵, 刘阳.基于倾斜摄影实景三维模型单体化分类与应用[J].北京测绘, 2018, 32(4):409-414.
[7] 王秋萍, 张志祥, 朱旭芳.图像分割方法综述[J]. 信息记录材料, 2019, 20(7):12-14.
[8] 刘扬, 付征叶, 郑逢斌. 高分辨率遥感影像目标分类与识别研究进展[J]. 地球信息科学学报, 2015, 17(9):1080-1091.
[9] BLASCHKE T. Object based image analysis for remote sensing[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2010, 65(1):2-16.
[10] PI Yalong, NATH N D, BEHZADAN A H. Convolutional neural networks for object detection in aerial imagery for disaster response and recovery[J]. Advanced Engineering Informatics, 2020, 43:101009.
[11] GARCIA-GARCIA A, ORTS-ESCOLANO S, OPREA S, et al. A survey on deep learning techniques for image and video semantic segmentation[J]. Applied Soft Computing, 2018, 70:41-65.
[12] CORDTS M, OMRAN M, RAMOS S, et al. The cityscapes dataset for semantic urban scene understanding[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV:IEEE, 2016:3213-3223.
[13] RUSSAKOVSKY O, DENG Jia, SU Hao, et al. ImageNet large scale visual recognition challenge[J]. International Journal of Computer Vision, 2015, 115(3):211-252.
[14] NOGUEIRA K, DOS SANTOS J A, CANCIAN L, et al. Semantic segmentation of vegetation images acquired by unmanned aerial vehicles using an ensemble of ConvNets[C]//Proceedings of 2017 IEEE International Geoscience and Remote Sensing Symposium. Fort Worth, TX:IEEE, 2017:3787-3790.
[15] WANG Shengke, LIU Lu, QU Liang, et al. Accurate ulva prolifera regions extraction of UAV images with superpixel and CNNs for ocean environment monitoring[J]. Neurocomputing, 2019, 348:158-168.
[16] 季顺平, 魏世清. 遥感影像建筑物提取的卷积神经元网络与开源数据集方法[J]. 测绘学报, 2019, 48(4):448-459.
[17] 张永军, 万一, 史文中, 等. 多源卫星影像的摄影测量遥感智能处理技术框架与初步实践[J]. 测绘学报, 2021, 50(8):1068-1083.
[18] WANG Panqu, CHEN Pengfei, YUAN Ye, et al. Understanding convolution for semantic segmentation[C]//Proceedings of 2018 IEEE Winter Conference on Applications of Computer Vision. Lake Tahoe, NV:IEEE, 2018:1451-1460.
[19] WEI Yunchao, LIANG Xiaodan, CHEN Yunpeng, et al. Learning to segment with image-level annotations[J]. Pattern Recognition, 2016, 59:234-244.
[20] WANG Juhong, LIU Bin, XU Kun. Semantic segmentation of high-resolution images[J]. Science China Information Sciences, 2017, 60(12):123101.
[21] 周飞燕, 金林鹏, 董军. 卷积神经网络研究综述[J]. 计算机学报, 2017, 40(6):1229-1251.
[22] 曲景影, 孙显, 高鑫. 基于CNN模型的高分辨率遥感图像目标识别[J]. 国外电子测量技术, 2016, 35(8):45-50.
[23] 许夙晖, 慕晓冬, 赵鹏, 等. 利用多尺度特征与深度网络对遥感影像进行场景分类[J]. 测绘学报, 2016, 45(7):834-840.