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Proceedings Paper

Friend or foe: exploiting sensor failures for transparent object localization and classification
Author(s): Viktor Seib; Andreas Barthen; Philipp Marohn; Dietrich Paulus
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Paper Abstract

In this work we address the problem of detecting and recognizing transparent objects using depth images from an RGB-D camera. Using this type of sensor usually prohibits the localization of transparent objects since the structured light pattern of these cameras is not reflected by transparent surfaces. Instead, transparent surfaces often appear as undefined values in the resulting images. However, these erroneous sensor readings form characteristic patterns that we exploit in the presented approach. The sensor data is fed into a deep convolutional neural network that is trained to classify and localize drinking glasses. We evaluate our approach with four different types of transparent objects. To our best knowledge, no datasets offering depth images of transparent objects exist so far. With this work we aim at closing this gap by providing our data to the public.

Paper Details

Date Published: 8 February 2017
PDF: 5 pages
Proc. SPIE 10253, 2016 International Conference on Robotics and Machine Vision, 102530I (8 February 2017); doi: 10.1117/12.2266255
Show Author Affiliations
Viktor Seib, Univ. Koblenz-Landau (Germany)
Andreas Barthen, Univ. Koblenz-Landau (Germany)
Philipp Marohn, Univ. Koblenz-Landau (Germany)
Dietrich Paulus, Univ. Koblenz-Landau (Germany)


Published in SPIE Proceedings Vol. 10253:
2016 International Conference on Robotics and Machine Vision
Alexander V. Bernstein; Adrian Olaru; Jianhong Zhou, Editor(s)

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