The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 157–162, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-157-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 157–162, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-157-2020

  12 Aug 2020

12 Aug 2020

3D DATA GENERATION USING LOW-COST CROSS-VIEW IMAGES

R. Qin1,2, S. Song1, and X. Huang1 R. Qin et al.
  • 1Dept. of Civil, Environmental and Geodetic Engineering, the Ohio State University (OSU), 2070 Neil Ave., Columbus, OH, USA
  • 2Dept. of Electrical Computer Engineering, OSU, 2015 Neil Ave., Columbus, OH, USA

Keywords: Cross-view images, Multi-view stereo (MVS) matching, 3D geo-registration, 3D meshing

Abstract. 3D data generation often requires expensive data collection such as aerial photogrammetric or LiDAR flight. In cases such data are unavailable, for example, areas of interest inaccessible from aerial platforms, alternative sources to be considered can be quite heterogeneous and come in the form of different accuracy, resolution and views, which challenge the standard data processing workflows. Assuming only overview satellite and ground-level go-pro images are available, which we call cross-view data due to the significant view differences, this paper introduces a framework from our project, consisting of a few novel algorithms that convert such challenging dataset to 3D textured mesh models containing both top and façade features. The necessary methods include 3D point cloud generation from satellite overview images and ground-level images, geo-registration and meshing. We firstly introduce the problems and discuss the potential challenges and introduce our proposed methods to address these challenges. Finally, we practice our proposed framework on a dataset consisting of twelve satellite images and 150k video frames acquired through a vehicle-mounted Go-pro camera and demonstrate the reconstruction results. We have also compared our results with results generated from an intuitive processing pipeline that involves typical geo-registration and meshing methods.