3D model retrieval based on interactive attention CNN and multiple features

Abstract

3D (three-dimensional) models are widely applied in our daily life, such as mechanical manufacture, games, biochemistry, art, virtual reality, and etc. With the exponential growth of 3D models on web and in model library, there is an increasing need to retrieve the desired model accurately according to freehand sketch. Researchers are focusing on applying machine learning technology to 3D model retrieval. In this article, we combine semantic feature, shape distribution features and gist feature to retrieve 3D model based on interactive attention convolutional neural networks (CNN). The purpose is to improve the accuracy of 3D model retrieval. Firstly, 2D (two-dimensional) views are extracted from 3D model at six different angles and converted into line drawings. Secondly, interactive attention module is embedded into CNN to extract semantic features, which adds data interaction between two CNN layers. Interactive attention CNN extracts effective features from 2D views. Gist algorithm and 2D shape distribution (SD) algorithm are used to extract global features. Thirdly, Euclidean distance is adopted to calculate the similarity of semantic feature, the similarity of gist feature and the similarity of shape distribution feature between sketch and 2D view. Then, the weighted sum of three similarities is used to compute the similarity between sketch and 2D view for retrieving 3D model. It solves the problem that low accuracy of 3D model retrieval is caused by the poor extraction of semantic features. Nearest neighbor (NN), first tier (FT), second tier (ST), F-measure (E(F)), and discounted cumulated gain (DCG) are used to evaluate the performance of 3D model retrieval. Experiments are conducted on ModelNet40 and results show that the proposed method is better than others. The proposed method is feasible in 3D model retrieval.

Reasoning with the Inverse of 3D Cardinal Direction Relations Based on Direction Matrices

“…It is widely used in many fields such as Spatial Intelligent Analysis and Processing, Urban Pipeline Network, Robot Navigation, Disaster Prevention and Mitigation, etc [1,2].And it is increasingly becoming a hot and difficult problem in the fields of Data Modeling, Cartographic Synthesis, Multimedia Design, Image Retrieval, etc [3]. The expression and reasoning with direction relation is a foundmental problem in the research field of spatial direction relations [4], which is important in Spatial Retrieval [5,6], Spatial Localization [7,8], Quality Inspection [9,10] and Spatial Storage [11] in the field of spatial database.…”