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Learning with Latent Representations of 3D Data: from Classical Methods to 3D Deep Learning

Wu, Chengzhi

Abstract (englisch):

3D data contain rich information about the full geometry of objects or scenes. Learning tasks on them have always been considered as hard ones in the computer vision community due to their extreme high dimensionality. Hence, latent representations of 3D geometries are often used to lower the data dimensionality for better parameterization and easier computation. In this report, we make a brief review on those latent representations obtained via different methods including classical ones and the emerging neural learning-based ones. Furthermore, the nowadays widely used deep learning methods have also been more closely investigated regarding their applications on various 3D data formats. The possibility of combing those two kinds of methods has also been addressed.

Verlagsausgabe §
DOI: 10.5445/IR/1000126700
Veröffentlicht am 30.11.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2020
Sprache Englisch
Identifikator ISBN: 978-3-7315-1028-4
ISSN: 1863-6489
KITopen-ID: 1000126700
Erschienen in Proceedings of the 2019 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory. Hrsg.: J. Beyerer; T. Zander
Verlag KIT Scientific Publishing
Seiten 133-149
Serie Karlsruher Schriften zur Anthropomatik / Lehrstuhl für Interaktive Echtzeitsysteme, Karlsruher Institut für Technologie ; Fraunhofer-Inst. für Optronik, Systemtechnik und Bildauswertung IOSB Karlsruhe ; 45
Bemerkung zur Veröffentlichung Technical Report IES-2019-11
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