Abstract
Abstract. Research on 3D urban modelling has been actively carried out for a long time. Recently the need of 3D urban modelling research is increased rapidly due to improved geo-web services and popularized smart devices. Nowadays 3D urban models provided by, for example, Google Earth use aerial photos for 3D urban modelling but there are some limitations: immediate update for the change of building models is difficult, many buildings are without 3D model and texture, and large resources for maintaining and updating are inevitable. To resolve the limitations mentioned above, we propose a method for semi-automatic building modelling and façade texture mapping from mobile phone images and analyze the result of modelling with actual measurements. Our method consists of camera geometry estimation step, image matching step, and façade mapping step. Models generated from this method were compared with actual measurement value of real buildings. Ratios of edge length of models and measurements were compared. Result showed 5.8% average error of length ratio. Through this method, we could generate a simple building model with fine façade textures without expensive dedicated tools and dataset.
Highlights
Building models generated from this method will be compared with the actual length ratio of the real buildings
We design the algorithm as the following steps: camera geometry estimation, epipolar resampling, ROI input from a user, 3D point cloud generation, and 3D building modelling
Figure 7. 3D point clouds from MDR As we can see in Fig 7, we could get dense point clouds from mobile phone stereo images
Summary
To achieve these goals, we have to select low cost source data which can get enough façade data and enough parameter specification to apply photogrammetric theory. We have to select proper modelling and processing algorithm. We select smart phones which are commercialized widely and can achieve high resolution image . From our previous research (Ahn et al, 2014), photogrammetric possibility of smart phone images were verified. We try to design our algorithm appropriate to the current specification of smartphones: their camera quality, processing power and orientation accuracy. We assume user input to define the boundary of building façade and confine building models as a simple cube model. Building models generated from this method will be compared with the actual length ratio of the real buildings
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