Ensemble of PANORAMA-based convolutional neural networks for 3D model classification and retrieval

Abstract

A novel method for the classification and retrieval of 3D models is proposed; it exploits the 2D panoramic view representation of 3D models as input to an ensemble of convolutional neural networks which automatically compute the features. The first step of the proposed pipeline, pose normalization is performed using the SYMPAN method, which is also computed on the panoramic view representation. In the training phase, three panoramic views corresponding to the major axes, are used for the training of an ensemble of convolutional neural networks. the panoramic views consist of 3-channel images, containing the Spatial Distribution Map, the Normals’ Deviation Map and the magnitude of the Normals’ Devation Map Gradient Image. The proposed method aims at capturing feature continuity of 3D models, while simultaneously minimizing data preprocessing via the construction of an augmented image representation. It is extensively tested in terms of classification and retrieval accuracy on two standard large scale datasets: ModelNet and ShapeNet.