Abstract
Following the success of Convolutional Neural Networks on object recognition and image classification using 2D images; in this work the framework has been extended to process 3D data. However, many current systems require huge amount of computation cost for dealing with large amount of data. In this work, we introduce an efficient 3D volumetric representation for training and testing CNNs and we also build several datasets based on the volumetric representation of 3D digits, different rotations along the x, y and z axis are also taken into account. Unlike the normal volumetric representation, our datasets are much less memory usage. Finally, we introduce a model based on the combination of CNN models, the structure of the model is based on the classical LeNet. The accuracy result achieved is beyond the state of art and it can classify a 3D digit in around 9 ms.
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Acknowledgment
This work has been partially supported by Project Eyes of Things (EoT) Grant n. 643924 from the European Union’s Horizon 2020 Research and Innovation Program.
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Xu, X., Corrigan, D., Dehghani, A., Caulfield, S., Moloney, D. (2016). 3D Object Recognition Based on Volumetric Representation Using Convolutional Neural Networks. In: Perales, F., Kittler, J. (eds) Articulated Motion and Deformable Objects. AMDO 2016. Lecture Notes in Computer Science(), vol 9756. Springer, Cham. https://doi.org/10.1007/978-3-319-41778-3_15
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DOI: https://doi.org/10.1007/978-3-319-41778-3_15
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