Abstract
For decades scene reconstruction from multiple images is a topic in computer vision and photogrammetry communities. Typical applications require very precise reconstructions and are not bound to a limited computation time. Techniques for these applications are based on complete sets of images to compute the scene geometry. They require a huge amount of resources and computation time before delivering results for visualization or further processing.
In the application of disaster management these approaches are not an option since the reconstructed data has to be available as soon as possible. Especially, when it comes to Miniature Unmanned Aerial Vehicles (MUAVs) sending aerial images to a ground station wirelessly while flying, operators can use the 3D data to explore the virtual world and to control the MUAVs.
In this paper an incremental approach for dense reconstructions from sparse datasets is presented. Instead of focussing on complete datasets and delivering results at the end of the computation process, our incremental approach delivers reasonable results while computing, for instance, to quickly visualize the virtual world or to create obstacle maps.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Roters, J., Steinicke, F., Hinrichs, K.H.: Quasi-real-time 3d reconstruction from low-altitude aerial images. In: Zlatanova, S., Ledoux, H., Fendel, E., Rumor, M. (eds.) Proc. of the 28th Urban Data Management Symposium, pp. 231–241 (2011)
Vierjahn, T., Lorenz, G., Mostafawy, S., Hinrichs, K.H.: Growing cell structures learning a progressive mesh during surface reconstruction - a top-down approach. In: Andujar, C., Puppo, E. (eds.) EG 2012 - Short Papers, pp. 29–32 (2012)
Crandall, D., Owens, A., Snavely, N., Huttenlocher, D.P.: Discrete-continuous optimization for large-scale structure from motion. In: Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 3001–3008 (2011)
Gherardi, R., Farenzena, M., Fusiello, A.: Improving the efficiency of hierarchical structure-and-motion. In: Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1594–1600 (2010)
Furukawa, Y., Ponce, J.: Accurate, dense, and robust multi-view stereopsis. IEEE Trans. on Pattern Analysis and Machine Intelligence 32(8), 1362–1376 (2010)
Newcombe, R.A., Davison, A.J.: Live dense reconstruction with a single moving camera. In: Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1498–1505 (2010)
Tola, E., Lepetit, V., Fua, P.: Daisy: An efficient dense descriptor applied to wide baseline stereo. IEEE Trans. on Pattern Analysis and Machine Intelligence 32(5), 815–830 (2010)
Wan, Y., Miao, Z., Tang, Z., Wan, L., Wang, Z.: An efficient wide-baseline dense matching descriptor. IEICE Transactions 95-D(7), 2021–2024 (2012)
Alahi, A., Ortiz, R., Vandergheynst, P.: FREAK: Fast Retina Keypoint. In: Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 510–517 (2012)
Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press (2004)
Gruber, L., Gauglitz, S., Ventura, J., Zollmann, S., Huber, M., Schlegel, M., Klinker, G., Schmalstieg, D., Höllerer, T.: The city of sights: Design, construction, and measurement of an augmented reality stage set. In: Proc. of the 9th IEEE International Symposium on Mixed and Augmented Reality, pp. 157–163 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Roters, J., Jiang, X. (2013). Incremental Dense Reconstruction from Sparse 3D Points with an Integrated Level-of-Detail Concept. In: Jiang, X., Bellon, O.R.P., Goldgof, D., Oishi, T. (eds) Advances in Depth Image Analysis and Applications. WDIA 2012. Lecture Notes in Computer Science, vol 7854. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40303-3_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-40303-3_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40302-6
Online ISBN: 978-3-642-40303-3
eBook Packages: Computer ScienceComputer Science (R0)