Abstract
During the last few years, deeper and deeper networks have been constantly proposed for addressing computer vision tasks. Residual Networks (ResNets) are the latest advancement in the field of deep learning that led to remarkable results in several image recognition and detection tasks. In this work, we modify two variants of the original ResNets, i.e. Wide Residual Networks (WRNs) and Residual of Residual Networks (RoRs), to work on 3D data and investigate for the first time, to our knowledge, their performance in the task of 3D object classification. We use a dataset containing volumetric representations of 3D models so as to fully exploit the underlying 3D information and present evidence that ‘3D ResNets’ constitute a valuable tool for classifying objects on 3D data as well.
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Acknowledgements
The research leading to these results has received funding from the European Union H2020 Horizon Programme (2014–2020) under grant agreement 665066, project DigiArt (The Internet Of Historical Things And Building New 3D Cultural Worlds).
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Ioannidou, A., Chatzilari, E., Nikolopoulos, S., Kompatsiaris, I. (2019). 3D ResNets for 3D Object Classification. In: Kompatsiaris, I., Huet, B., Mezaris, V., Gurrin, C., Cheng, WH., Vrochidis, S. (eds) MultiMedia Modeling. MMM 2019. Lecture Notes in Computer Science(), vol 11295. Springer, Cham. https://doi.org/10.1007/978-3-030-05710-7_41
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