3DSliceLeNet: Recognizing 3D Objects Using a Slice-Representation

Francisco Gomez-Donoso, Félix Escalona, Sergio Orts-Escolano, Alberto Garcia-Garcia, José García Rodríguez, Miguel Cazorla

Abstract: Convolutional Neural Networks (CNNs) have become the default paradigm for addressing classification problems, especially, but not only, in image recognition. This is mainly due to their high success rate. Although a number of approaches currently apply deep learning to the 3D shape recognition problem, they are either too slow for online use or too error-prone. To fill this gap, we propose 3DSliceLeNet, a deep learning architecture for point cloud classification. Our proposal converts the input point clouds into a two-dimensional representation by performing a slicing process and projecting the points to the principal planes, thus generating images that are used by the convolutional architecture. 3DSliceLeNet successfully achieves both high accuracy and low computational cost. A dense set of experiments has been conducted to validate our system under the ModelNet challenge, a large-scale 3D Computer Aided Design (CAD) model dataset. Our proposal achieves a success rate of 94.37% and an Area under Curve (AUC) of 0.978 on the ModelNet-10 classification task.