Inline Double Layer Depth Estimation with Transparent Materials

Christian Kopf*, Thomas Pock, Bernhard Blaschitz, Svorad Štolc

*Corresponding author for this work

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


3D depth computation from stereo data has been one of the most researched topics in computer vision. While state-of-art approaches have flourished over time, reconstruction of transparent materials is still considered an open problem. Based on 3D light field data we propose a method to obtain smooth and consistent double-layer estimates of scenes with transparent materials. Our novel approach robustly combines estimates from models with different layer hypotheses in a cost volume with subsequent minimization of a joint second order TGV energy on two depth layers. Additionally we showcase the results of our approach on objects from common inspection use-cases in an industrial setting and compare our work to related methods.
Original languageEnglish
Title of host publicationPattern Recognition - 42nd DAGM German Conference, DAGM GCPR 2020, Proceedings
EditorsZeynep Akata, Andreas Geiger, Torsten Sattler
Place of PublicationCham
Number of pages14
ISBN (Electronic)978-3-030-71278-5
ISBN (Print)978-3-030-71277-8
Publication statusPublished - 17 Mar 2021
Event42nd German Conference on Pattern Recognition - Virtual, Virtuell, Germany
Duration: 28 Sep 20201 Oct 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12544 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference42nd German Conference on Pattern Recognition
Abbreviated titleDAGM GCPR 2020

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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