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Towards an efficient 3D model estimation methodology for aerial and ground images

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Abstract

In this paper we propose an efficient approach for automatic generation of 3D models from images based on structure from motion (SfM) and multi-view stereo reconstruction techniques. Current imaging devices are capable of producing high-definition images and are an ubiquitous payload of unmanned aerial vehicles. However, the time required to obtain models quickly becomes prohibitive as the number of images increases. In our approach, which is image-based only, we use meta-data information such as GPS, keypoint filtering and multiple local bundle adjustment refinement instead of global optimization in a novel scheme to speed up the incremental SfM process. The results from real data show that our approach outperforms the time performance of current strategies while maintaining the quality of the resulting model. Experiments with an unorganized set of images were also conducted, and the results show that our method is able to efficiently estimate 3D models from collections of images with reduced re-projection error.

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Notes

  1. Commercial off-the-shelf.

  2. This value was defined empirically, but it is not critical if it remains within the limits between \({\le }0.10\) or \({\ge }0.90\) according to our experiments.

  3. Points that have a triangulation angle higher than 2.0\(^{\circ }\).

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Acknowledgements

We thank the anonymous reviewers for their comments and insightful observations. This work is supported by CAPES, CNPq, FAPEMIG, and Vale Institute of Technology (ITV).

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Authors and Affiliations

  1. Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil

    Guilherme Potje, Gabriel Resende, Mario Campos & Erickson R. Nascimento

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  1. Guilherme Potje
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  2. Gabriel Resende
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  3. Mario Campos
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  4. Erickson R. Nascimento
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Correspondence to Guilherme Potje.

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Potje, G., Resende, G., Campos, M. et al. Towards an efficient 3D model estimation methodology for aerial and ground images. Machine Vision and Applications 28, 937–952 (2017). https://doi.org/10.1007/s00138-017-0875-x

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  • DOI: https://doi.org/10.1007/s00138-017-0875-x

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