Non Rigid 3D Shape Partial Matching Based on Deep Feature Fusion

Abstract

<p indent=0mm>To meet the requirements of massive and heterogeneous 3D shape partial matching and intelligent retrieval technology, a 3D shape local matching method based on the deep fusion feature of F-PointCNN is proposed. First, the feature bag (BoF) learning model is used to propose an geometric image representation, which can not only effectively distinguish heterogeneous non-rigid 3D models of the same kind, but also reveal the structural similarity of large-scale incomplete 3D models. Next, a cascaded convolutional neural network slearning framework (F-PointCNN) is constructed, where BoF-CNN learns the deep global feature from BoF geometric images and establishes the point feature representation that integrates the local feature and the global feature; Point-CNN refines the point feature and generates deep feature representation which effectively improves the discriminative ability and robustness. Finally, the local shape matching of non rigid 3D model is realized by cross matrix measurement. The open non-rigid 3D shape databases are used to carry out a series of experiments, the results show that the features extracted by proposed method have stronger discriminative ability in large-scale transformation shape classification and higher precision in partial shape matching.