An Octree-Based Approach towards Efficient Variational Range Data Fusion

By Wadim Kehl, Tobias Holl, Federico Tombari, Slobodan Ilic, Nassir Navab. 2016

7 citations recorded by the source corpus.

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.

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Citations in the corpus

OctNet: Learning Deep 3D Representations at High Resolutions (2016, background): “Areas of application include depth fusion [23], image rendering [26] and 3D reconstruction [44].”