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.
@article{arxiv.1608.07411,
title = {An Octree-Based Approach towards Efficient Variational Range Data Fusion},
author = {Wadim Kehl and Tobias Holl and Federico Tombari and Slobodan Ilic and Nassir Navab},
journal= {arXiv preprint arXiv:1608.07411},
year = {2016}
}