2DSlicesNet: A 2D Slice-Based Convolutional Neural Network for 3D Object Retrieval and Classification

Abstract

3D data can be instrumental to the computer vision field as it provides insightful information about the full 3D models' geometry. Recently, with easy access to both computational power and huge 3D databases, it is feasible to apply convolutional neural networks to automatically extract the 3D models' features. This paper presents a novel approach, called 2DSlicesNet, which deals with the issue of 3D model retrieval and classification using a 2D slice-based representation with a 3D convolutional neural network. The assumption in this context is that similar 3D models will be composed of almost identical 2D slices. Therefore, we first transform each normalized 3D model into a set of 2D slices corresponding to its first main axis, and then use them as input data to our 3D convolutional neural network. Experimental results and comparison with state-of-the-art approaches, using ModelNet10 and ModelNet40 datasets, prove that our proposed 2DSlicesNet approach can reach notable rates of accuracy in classification and retrieval.