Probabilistic Range Image Integration for DSM and True-Orthophoto Generation
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- 作者：Markus Rumpler (18)
Andreas Wendel (18)
Horst Bischof (18) - 刊名：Lecture Notes in Computer Science
- 出版年：2013
- 出版时间：2013
- 年：2013
- 卷：7944
- 期：1
- 页码：545-554
- 全文大小：4326KB
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Andreas Wendel (18)
Horst Bischof (18)
18. Institute for Computer Graphics and Vision, Graz University of Technology, Austria - ISSN：1611-3349
文摘
Typical photogrammetric processing pipelines for digital surface model (DSM) generation perform aerial triangulation, dense image matching and a fusion step to integrate multiple depth estimates into a consistent 2.5D surface model. The integration is strongly influenced by the quality of the individual depth estimates, which need to be handled robustly. We propose a probabilistically motivated 3D filtering scheme for range image integration. Our approach avoids a discrete voxel sampling, is memory efficient and can easily be parallelized. Neighborhood information given by a Delaunay triangulation can be exploited for photometric refinement of the fused DSMs before rendering true-orthophotos from the obtained models. We compare our range image fusion approach quantitatively on ground truth data by a comparison with standard median fusion. We show that our approach can handle a large amount of outliers very robustly and is able to produce improved DSMs and true-orthophotos in a qualitative comparison with current state-of-the-art commercial aerial image processing software.