TT-TSDF: Memory-Efficient TSDF with Low-Rank Tensor Train Decomposition

Alexey I. Boyko; Mikhail P. Matrosov; Ivan V. Oseledets; Dzmitry Tsetserukou; Gonzalo Ferrer

IROS 2020poster9 citations

TT-TSDF: Memory-Efficient TSDF with Low-Rank Tensor Train Decomposition

Alexey I. Boyko, Mikhail P. Matrosov, Ivan V. Oseledets, Dzmitry Tsetserukou, Gonzalo Ferrer

Abstract

In this paper we apply the low-rank Tensor Train decomposition for compression and operations on 3D objects and scenes represented by volumetric distance functions. Our study shows that not only it allows for a very efficient compression of the high-resolution TSDF maps (up to three orders of magnitude of the original memory footprint at resolution of 5123), but also allows to perform TSDF-Fusion directly in the low-rank form. This can potentially enable much more efficient 3D mapping on low-power mobile and consumer robot platforms.

BibTeX

@inproceedings{iros2020_tttsdfmemoryeffi,
  title = {TT-TSDF: Memory-Efficient TSDF with Low-Rank Tensor Train Decomposition},
  author = {Alexey I. Boyko and Mikhail P. Matrosov and Ivan V. Oseledets and Dzmitry Tsetserukou and Gonzalo Ferrer},
  booktitle = {IROS 2020},
  year = {2020}
}