Inline Double Layer Depth Estimation with Transparent Materials

Christian Kopf, Thomas Pock, Bernhard Blaschitz, Svorad Štolc

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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
Subtitle of host publication42nd DAGM German Conference, DAGM GCPR 2020, Tübingen, Germany, September 28 – October 1, 2020, Proceedings
EditorsZeynep Akata, Andreas Geiger, Torsten Sattler
Place of PublicationCham
Number of pages431
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


Conference42nd German Conference on Pattern Recognition
Abbreviated titleDAGM GCPR 2020

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