Abstract
Abstract. A variety of applications exist for aerial 3D reconstruction, ranging from the production of digital surface models (DSMs) and digital terrain models (DTMs) to the creation of true orthophoto and full 3D models of urban scenes that can be visualized through the web. In this paper we present an automated end-to-end workflow to create digital surface models from large scale and highly overlapping aerial images. The core component of our approach is a multi-view dense matching algorithm that fully exploits the redundancy of the data. This is in contrast to traditional two-view based stereo matching approaches in aerial photogrammetry. In particular, our solution to dense depth estimation is based on a multi-view plane sweep approach with discontinuity preserving global optimization. We provide a fully automatic framework for aerial triangulation, image overlap estimation and dense depth matching. Our algorithms are designed to run on current graphics processing units (GPUs) which makes large scale processing feasible at low cost. We present dense matching results from a large aerial survey comprising 3000 aerial images of Graz and give a detailed performance analysis in terms of accuracy and processing time.
Highlights
Novel digital aerial cameras capture high resolution images that are readily suitable for the creation of photogrammetric end products like digital surface models (DSMs), digital terrain models (DTMs), orthophotos and full 3D models of urban scenes that can be visualized trough the web (Zebedin, 2010)
Under the premise that processing of such a massive amount of data can be done in reasonable time, passive photogrammetry can directly outperform current LiDAR systems (Baltsavias, 1999) by means of ground sampling distance (GSD) and reduced flight costs (Leberl et al, 2010)
With a further increase of ground sampling distance to the range of about 1cm, aerial photogrammetry could even compete with traditional ground measurement devices such as surveys based on total stations and GNSS/GPS systems
Summary
Novel digital aerial cameras capture high resolution images that are readily suitable for the creation of photogrammetric end products like digital surface models (DSMs), digital terrain models (DTMs), orthophotos and full 3D models of urban scenes that can be visualized trough the web (Zebedin, 2010). Under the premise that processing of such a massive amount of data can be done in reasonable time, passive photogrammetry can directly outperform current LiDAR systems (Baltsavias, 1999) by means of ground sampling distance (GSD) and reduced flight costs (Leberl et al, 2010). In this paper we employ a fully automated processing pipeline that computes the scene structure and camera orientations from aerial input images. In computer vision, this approach is known as Structure from Motion (SfM) and delivers subpixel accurate photo alignment from image measurements, only.
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