SDF-2-SDF: Highly accurate 3D object reconstruction

Miroslava Slavcheva, Wadim Kehl, Nassir Navab, Slobodan Ilic

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

19 Scopus citations

Abstract

This paper addresses the problem of 3D object reconstruction using RGB-D sensors.Our main contribution is a novel implicit-to-implicit surface registration scheme between signed distance fields (SDFs), utilized both for the real-time frame-to-frame camera tracking and for the subsequent global optimization. SDF-2-SDF registration circumvents expensive correspondence search and allows for incorporation of multiple geometric constraints without any dependence on texture, yielding highly accurate 3Dmodels.An extensive quantitative evaluation on real and synthetic data demonstrates improved tracking and higher fidelity reconstructions than a variety of state-of-the-art methods. We make our data publicly available, creating the first object reconstruction dataset to include ground-truth CAD models and RGB-D sequences from sensors of various quality.

Original languageEnglish
Title of host publicationComputer Vision - 14th European Conference, ECCV 2016, Proceedings
EditorsBastian Leibe, Jiri Matas, Nicu Sebe, Max Welling
PublisherSpringer Verlag
Pages680-696
Number of pages17
ISBN (Print)9783319464473
DOIs
StatePublished - 2016
Event14th European Conference on Computer Vision, ECCV 2016 - Amsterdam, Netherlands
Duration: 8 Oct 201616 Oct 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9905 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th European Conference on Computer Vision, ECCV 2016
Country/TerritoryNetherlands
CityAmsterdam
Period8/10/1616/10/16

Keywords

  • Object reconstruction
  • RGB-Dsensors
  • Signed distance field

Fingerprint

Dive into the research topics of 'SDF-2-SDF: Highly accurate 3D object reconstruction'. Together they form a unique fingerprint.

Cite this