Automatic, Multiview, Coplanar Extraction for CityGML Building Model Texture Mapping

Haiqing He,Taiqing Lin,Jing Yu,Yuqian Wang,Yufeng Zhu,Penggen Cheng,Guoqiang Dai

doi:10.3390/rs14010050

Abstract

Most 3D CityGML building models in street-view maps (e.g., Google, Baidu) lack texture information, which is generally used to reconstruct real-scene 3D models by photogrammetric techniques, such as unmanned aerial vehicle (UAV) mapping. However, due to its simplified building model and inaccurate location information, the commonly used photogrammetric method using a single data source cannot satisfy the requirement of texture mapping for the CityGML building model. Furthermore, a single data source usually suffers from several problems, such as object occlusion. We proposed a novel approach to achieve CityGML building model texture mapping by multiview coplanar extraction from UAV remotely sensed or terrestrial images to alleviate these problems. We utilized a deep convolutional neural network to filter out object occlusion (e.g., pedestrians, vehicles, and trees) and obtain building-texture distribution. Point-line-based features are extracted to characterize multiview coplanar textures in 2D space under the constraint of a homography matrix, and geometric topology is subsequently conducted to optimize the boundary of textures by using a strategy combining Hough-transform and iterative least-squares methods. Experimental results show that the proposed approach enables texture mapping for building façades to use 2D terrestrial images without the requirement of exterior orientation information; that is, different from the photogrammetric method, a collinear equation is not an essential part to capture texture information. In addition, the proposed approach can significantly eliminate blurred and distorted textures of building models, so it is suitable for automatic and rapid texture updates.

Highlights

We propose a novel approach of texture mapping for 3D building models from multisource data, such unmanned aerial vehicle (UAV) remotely sensed imagery and terrestrial images, to alleviate these problems of texture mapping for city geography markup language (CityGML) building models
The datasets mainly include two categories, as follows: (1) the untextured building models downloaded from the commercial map providers, such as Baidu, and (2) the textured building models derived from the photogrammetric method
Relatively regular and simplified building models, such as LOD2 CityGML building models, are selected to evaluate the proposed approach because this study mainly focuses on the texture mapping of nondetailed building models

Summary

Introduction

The development of smart city highly depends on the quality of geospatial data infrastructure, and 3D visualization is a core technology of a digital city [1]. A representative city geography markup language (CityGML) is developed by Open Geospatial Consortium for defining and describing 3D building attributes, such as geometric, topological, semantic, and appearance characteristics, which are very valuable for many applications, such as simulation modeling, urban planning, and map navigation [2]. Texture mapping of building models has always been a hot and significant research topic in the fields of computer vision, photogrammetry, and remote sensing. Due to problems such as ground-object occlusion, texture mapping of CityGML building models is still challenging. CityGML can be divided into five levels of detail (LOD), including LOD0

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