An Octree-Based Approach towards Efficient Variational Range Data Fusion

Wadim Kehl, Tobias Holl, Federico Tombari, Slobodan Ilic and Nassir Navab

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.

Session

Posters 1

Files

Extended Abstract (PDF, 2M)
Paper (PDF, 8M)

DOI

10.5244/C.30.21
https://dx.doi.org/10.5244/C.30.21

Citation

Bibtex

        @inproceedings{BMVC2016_21,
        	title={An Octree-Based Approach towards Efficient Variational Range Data Fusion},
        	author={Wadim Kehl, Tobias Holl, Federico Tombari, Slobodan Ilic and Nassir Navab},
        	year={2016},
        	month={September},
        	pages={21.1-21.12},
        	articleno={21},
        	numpages={12},
        	booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
        	publisher={BMVA Press},
        	editor={Richard C. Wilson, Edwin R. Hancock and William A. P. Smith},
        	doi={10.5244/C.30.21},
        	isbn={1-901725-59-6},
        	url={https://dx.doi.org/10.5244/C.30.21}
        }