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

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages10116-10121
Number of pages6
ISBN (Electronic)9781728162126
DOIs
Publication statusPublished - 24 Oct 2020
Event2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020 - Las Vegas, United States
Duration: 24 Oct 202024 Jan 2021

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
Country/TerritoryUnited States
CityLas Vegas
Period24/10/2024/01/21

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