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Real-Time Texturing for 6D Object Instance Detection from RGB Images

Rojtberg, Pavel ; Kuijper, Arjan (2019)
Real-Time Texturing for 6D Object Instance Detection from RGB Images.
2019 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct). Beijing, China (10.10.2019-18.10.2019)
doi: 10.1109/ISMAR-Adjunct.2019.00-25
Konferenzveröffentlichung, Bibliographie

Kurzbeschreibung (Abstract)

For objected detection, the availability of color cues strongly influences detection rates and is even a prerequisite for many methods. However, when training on synthetic CAD data, this information is not available. We therefore present a method for generating a texture-map from image sequences in real-time. The method relies on 6 degree-of-freedom poses and a 3D-model being available. In contrast to previous works this allows interleaving detection and texturing for upgrading the detector on-the-fly. Our evaluation shows that the acquired texture-map significantly improves detection rates using the LINEMOD [5] detector on RGB images only. Additionally, we use the texture-map to differentiate instances of the same object by surface color.

Typ des Eintrags: Konferenzveröffentlichung
Erschienen: 2019
Autor(en): Rojtberg, Pavel ; Kuijper, Arjan
Art des Eintrags: Bibliographie
Titel: Real-Time Texturing for 6D Object Instance Detection from RGB Images
Sprache: Englisch
Publikationsjahr: 2019
Veranstaltungstitel: 2019 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)
Veranstaltungsort: Beijing, China
Veranstaltungsdatum: 10.10.2019-18.10.2019
DOI: 10.1109/ISMAR-Adjunct.2019.00-25
URL / URN: https://doi.org/10.1109/ISMAR-Adjunct.2019.00-25
Kurzbeschreibung (Abstract):

For objected detection, the availability of color cues strongly influences detection rates and is even a prerequisite for many methods. However, when training on synthetic CAD data, this information is not available. We therefore present a method for generating a texture-map from image sequences in real-time. The method relies on 6 degree-of-freedom poses and a 3D-model being available. In contrast to previous works this allows interleaving detection and texturing for upgrading the detector on-the-fly. Our evaluation shows that the acquired texture-map significantly improves detection rates using the LINEMOD [5] detector on RGB images only. Additionally, we use the texture-map to differentiate instances of the same object by surface color.

Freie Schlagworte: Artificial intelligence (AI) Modeling of physical attributes Recovery of physical attributes Pattern recognition Implementations Interactive systems
Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Graphisch-Interaktive Systeme
20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing
Hinterlegungsdatum: 09 Apr 2020 10:48
Letzte Änderung: 09 Apr 2020 10:48
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