Scalable Visual Exploration of 3D Shape Databases via Feature Synthesis and Selection

Abstract

We present a set of techniques to address the problem of scalable creation of visual overview representations of large 3D shape databases based on dimensionality reduction of feature vectors extracted from shape descriptions. We address the problem of feature extraction by exploring both combinations of hand-engineered geometric features and using the latent feature vectors generated by a deep learning classification method, and discuss the comparative advantages of both approaches. Separately, we address the problem of generating insightful 2D projections of these feature vectors that are able to separate well different groups of similar shapes by two approaches. First, we create quality projections by both automatic search in the space of feature combinations and, alternatively, by leveraging human insight to improve projections by iterative feature selection. Secondly, we use deep learning to automatically construct projections from the extracted features. We show that our three variations of deep learning, which jointly treat feature extraction, selection, and projection, allow efficient creation of high-quality visual overviews of large shape collections, require minimal user intervention, and are easy to implement. We demonstrate our approach on several real-world 3D shape databases.