3D Object Recognition Method Using CNNs and Slicing

Three-Dimensional (3D) object recognition has been receiving more and more attention in the field of Computer Vision, as the development of AI algorithms and advancements of hardware components allow for better results nowadays more than ever before. Neural Networks are used to better classify different types of objects, these are being fed with pre-processed 3D objects represented in a 2D domain for better highlighting the important features and for a better performance. In this paper, we propose a strategy of feeding 2D representations of 3D meshes made by using different types of slicing to a CNN. In this way, we want to minimize the information loss and the redundancy the data suffers going from a 3D representation to a 2D one. To assess the results, we used Princeton’s state-of-the-art ModelNet10 dataset, consisting of 4899 3D objects divided into 10 categories.