Abstract
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.
Highlights
1.1 IntroductionCity-scale data generation often requires expensive data collection such as aerial photogrammetric or LiDAR flight (Haala and Cavegn, 2016; Schwarz, 2010)
We introduce in our proposed framework three major contributions to address the above-mentioned challenges, these being: 1) we introduce a monocular video-frame based 3D reconstruction pipeline to achieve the minimal geometric distortion by leveraging the speed and accuracy in a photogrammetric reconstruction pipeline; 2) we introduce a novel cross-view geo-registration algorithm that takes point clouds generated from satellite multi-view stereo (MVS) images and from street-view videos, to co-register the street-view point clouds to the overview point clouds; 3) we extend the existing mesh approaches to accommodate point clouds with images coming from different cameras
4.2 Experiment Results We demonstrate that the resulting geometry shows completeness in terms of the rooftop and façade information
Summary
City-scale data generation often requires expensive data collection such as aerial photogrammetric or LiDAR flight (Haala and Cavegn, 2016; Schwarz, 2010). There exist a large number of street-view images coming either from crowdsourcing platforms or collected using relatively cheap equipment (e.g. video frames from low-cost cameras) that provides high-resolution information of object facades. Both the overview and the street-view data are complementary to each other and their view differences being approximately 90°forms cross-view dataset, a combined use of which may yield a low-cost solution for city-scale 3D modelling.
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