An octree-based approach towards efficient variational range data fusion
Kehl W, Holl T, Tombari F, Ilic S, Navab N (2016)
Publication Type: Conference contribution
Publication year: 2016
Publisher: British Machine Vision Conference, BMVC
Book Volume: 2016-September
Pages Range: 21.1-21.12
Conference Proceedings Title: British Machine Vision Conference 2016, BMVC 2016
Event location: York, GBR
DOI: 10.5244/C.30.21
Abstract
Volume-based reconstruction is usually expensive both in terms of memory consumption and runtime. Especially for sparse geometric structures, volumetric representations produce a huge computational overhead. We present an efficient way to fuse range data via a variational Octree-based minimization approach by taking the actual range data geometry into account. We transform the data into Octree-based truncated signed distance fields and show how the optimization can be conducted on the newly created structures. The main challenge is to uphold speed and a low memory footprint without sacrificing the solutions’ accuracy during optimization. We explain how to dynamically adjust the optimizer’s geometric structure via joining/splitting of Octree nodes and how to define the operators. We evaluate on various datasets and outline the suitability in terms of performance and geometric accuracy.
Involved external institutions
Germany (DE)How to cite
APA:
Kehl, W., Holl, T., Tombari, F., Ilic, S., & Navab, N. (2016). An octree-based approach towards efficient variational range data fusion. In British Machine Vision Conference 2016, BMVC 2016 (pp. 21.1-21.12). York, GBR: British Machine Vision Conference, BMVC.
MLA:
Kehl, Wadim, et al. "An octree-based approach towards efficient variational range data fusion." Proceedings of the 27th British Machine Vision Conference, BMVC 2016, York, GBR British Machine Vision Conference, BMVC, 2016. 21.1-21.12.
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