LonchaNet: A sliced-based CNN architecture for real-time 3D object recognition

Abstract

In the last few years, Convolutional Neural Networks (CNNs) had become the default paradigm to address classification problems, specially, but not only, in image recognition. This is mainly due to the high success rate that they provide. Despite there currently exist approaches that apply deep learning to the 3D recognition problem, they are either too slow for online uses or too error prone. To fill this gap, we propose LonchaNet, a deep learning architecture for point clouds classification. Our system successfully achieves a high accuracy yet providing a low computation cost. A dense set of experiments were carried out in order to validate our system in the frame of the ModelNet - a large-scale 3D CAD models dataset - challenge. Our proposal achieves a success rate of 94.37% in the ModelNet-10 classification task, the second place in the leaderboard as of today (November, 2016).