三维激光扫描与虚拟现实技术在城市景观中的应用
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摘要
为了提高城市景观虚拟现实设计的三维配准性能,降低城市景观虚拟现实设计误差,采用三维激光扫描和虚拟呈现技术进行城市景观虚拟现实设计,提出一种基于三维激光扫描的帧重组和纹理映射的城市景观设计技术。根据激光三维扫描成像的中心像素点进行城市景观设计的三维结构动态重组,采用多小波尺度分割技术进行图像虚拟重构和滤波处理,结合自适应特征分解方法进行城市景观图像的点扫描和动态识别,采用纹理映射方法对城市景观的激光三维扫描图像光线逐层遍历,采用三维激光扫描技术进行城市景观设计中的虚拟现实视景仿真,实现城市景观虚拟现实设计。仿真测试结果表明,采用该方法进行城市景观的是三维虚拟现实设计的动态效果较好,设计景观的逼真度较高,景观设计的自动展示能力较强。
In order to improve the 3D registration performance of the urban landscape virtual reality design and reduce the design error of the urban landscape virtual reality design,3D laser scanning and virtual presentation technology are used tothe urban landscape virtual reality design. An urban landscape design technique based on 3D laser scanning frame recombination and texture mapping is proposed. According to the center pixel of laser 3D scanning imaging,the 3D structure of urban landscape design is dynamically reorganized,and the multi-wavelet scale segmentation technique is used to reconstruct and filter the image. The point scanning and dynamic recognition of urban landscape images are carried out by combining the adaptive feature decomposition method. The method of texture mapping is used to traverse the 3D laser scanning images of urban landscape layer by layer. The virtual reality scene simulation in urban landscape design is carried out by using 3D laser scanning technology to realize the virtual reality design of urban landscape. The simulation results show that the dynamic effect of 3D virtual reality design is better,the fidelity of design landscape is higher,and the automatic display ability of landscape design is stronger.
In order to improve the 3D registration performance of the urban landscape virtual reality design and reduce the design error of the urban landscape virtual reality design,3D laser scanning and virtual presentation technology are used tothe urban landscape virtual reality design. An urban landscape design technique based on 3D laser scanning frame recombination and texture mapping is proposed. According to the center pixel of laser 3D scanning imaging,the 3D structure of urban landscape design is dynamically reorganized,and the multi-wavelet scale segmentation technique is used to reconstruct and filter the image. The point scanning and dynamic recognition of urban landscape images are carried out by combining the adaptive feature decomposition method. The method of texture mapping is used to traverse the 3D laser scanning images of urban landscape layer by layer. The virtual reality scene simulation in urban landscape design is carried out by using 3D laser scanning technology to realize the virtual reality design of urban landscape. The simulation results show that the dynamic effect of 3D virtual reality design is better,the fidelity of design landscape is higher,and the automatic display ability of landscape design is stronger.
引文
[1]张良,罗祎敏,马洪超,等.基于未标签信息主动学习算法的高光谱影像分类[J].计算机应用,2017,37(6):1768-1771.
[2]胡健,罗迎,张群,孙玉雪,等弹道目标宽带雷达干涉式三维成像与微动特征提取[J].电子与信息学报,2017,39(8):1865-1871.
[3]施文灶,毛政元.基于图分割的高分辨率遥感影像建筑物变化检测研究[J].地球信息科学学报,2016,18(3):423-432.
[4]WEI X S,LUO J H,WU J.Selective convolutional descriptor aggregation for fine-grained image retrieval[J].IEEETransactions on Image Processing,2017,26(6):2868-2881.
[5]RAZAVIAN A S,SULLIVAN J,CARLSSON S.Visual instance retrieval with deep convolutional networks[J].ITETransactions on Media Technology and Applications,2016,4(3):251-258.
[6]GUO H,LIU H,WU C,et al.Logistic discrimination based on G-mean and F-measure for imbalanced problem[J].Journal of Intelligent and Fuzzy Systems,2016,31(3):1155-1166.
[7]张宝华,刘鹤.基于区域定位与轮廓分割的红外目标检测[J].激光技术,2015,39(6):840-844.
[8]周兴,但旦,千佳,等.结构光照明显微中的超分辨图像重建研究[J].光学学报,2017,25(3):10-21.
[9]郭华平,董亚东,毛海涛,等.一种基于逻辑判别式的稀有类分类方法[J].小型微型计算机系统,2016,37(1):140-145.
[10]JU B,QIAN Y T,YE M C.Collaborative filtering algorithm based on structured projective nonnegative matrix factorization[J].Journal of Zhejiang University:Engineering Science,2015,49(7):1319-1325.
[11]LI B F,TANG Y D,HAN Z.A geometric structure preserving nonnegative matrix factorization for data representation[J].Information and Control,2017,46(1):53-59,64.
[12]WU Y,SHEN B,LING H.Visual tracking via online nonnegative matrix factorization[J].IEEE Transactions on Circuits and Systems for Video Technology,2014,24(3):374-383.
[13]HUANG S C,LIU Y.Classification algorithm for noisy and dynamic data stream[J].Journal of Jiangsu University of Science and Technology(Natural Science Edition),2016,30(3):281-285.
[14]苏衡,周杰,张志浩.超分辨率图像重建方法综述[J].自动化学报,2013,39(8):1202-1213.
[15]周靖鸿,周璀,朱建军,等.基于非下采样轮廓波变换遥感影像超分辨重建方法[J].光学学报,2015,35(1):106-114
[16]周飞,贾振红,杨杰,等.基于剪切波域改进Gamma校正的医学图像增强算法[J].光电子·激光,2017,28(5):566-572.
[17]邢凯盛,凌有铸,陈孟元.ORB特征匹配的误匹配点剔除算法研究[J].电子测量与仪器学报,2016,30(8):1255-1262.
[2]胡健,罗迎,张群,孙玉雪,等弹道目标宽带雷达干涉式三维成像与微动特征提取[J].电子与信息学报,2017,39(8):1865-1871.
[3]施文灶,毛政元.基于图分割的高分辨率遥感影像建筑物变化检测研究[J].地球信息科学学报,2016,18(3):423-432.
[4]WEI X S,LUO J H,WU J.Selective convolutional descriptor aggregation for fine-grained image retrieval[J].IEEETransactions on Image Processing,2017,26(6):2868-2881.
[5]RAZAVIAN A S,SULLIVAN J,CARLSSON S.Visual instance retrieval with deep convolutional networks[J].ITETransactions on Media Technology and Applications,2016,4(3):251-258.
[6]GUO H,LIU H,WU C,et al.Logistic discrimination based on G-mean and F-measure for imbalanced problem[J].Journal of Intelligent and Fuzzy Systems,2016,31(3):1155-1166.
[7]张宝华,刘鹤.基于区域定位与轮廓分割的红外目标检测[J].激光技术,2015,39(6):840-844.
[8]周兴,但旦,千佳,等.结构光照明显微中的超分辨图像重建研究[J].光学学报,2017,25(3):10-21.
[9]郭华平,董亚东,毛海涛,等.一种基于逻辑判别式的稀有类分类方法[J].小型微型计算机系统,2016,37(1):140-145.
[10]JU B,QIAN Y T,YE M C.Collaborative filtering algorithm based on structured projective nonnegative matrix factorization[J].Journal of Zhejiang University:Engineering Science,2015,49(7):1319-1325.
[11]LI B F,TANG Y D,HAN Z.A geometric structure preserving nonnegative matrix factorization for data representation[J].Information and Control,2017,46(1):53-59,64.
[12]WU Y,SHEN B,LING H.Visual tracking via online nonnegative matrix factorization[J].IEEE Transactions on Circuits and Systems for Video Technology,2014,24(3):374-383.
[13]HUANG S C,LIU Y.Classification algorithm for noisy and dynamic data stream[J].Journal of Jiangsu University of Science and Technology(Natural Science Edition),2016,30(3):281-285.
[14]苏衡,周杰,张志浩.超分辨率图像重建方法综述[J].自动化学报,2013,39(8):1202-1213.
[15]周靖鸿,周璀,朱建军,等.基于非下采样轮廓波变换遥感影像超分辨重建方法[J].光学学报,2015,35(1):106-114
[16]周飞,贾振红,杨杰,等.基于剪切波域改进Gamma校正的医学图像增强算法[J].光电子·激光,2017,28(5):566-572.
[17]邢凯盛,凌有铸,陈孟元.ORB特征匹配的误匹配点剔除算法研究[J].电子测量与仪器学报,2016,30(8):1255-1262.