Semantics-Driven Approach for Automatic Selection of Best Views of 3D Shapes

Abstract

We introduce a new framework for the automatic selection of the best views of 3D models. The approach is based on the assumption that models belonging to the same class of shapes share the same salient features that discriminate them from the models of other classes. The main issue is learning these features. We propose a datadriven approach where the best view selection problem is formulated as a classification and feature selection problem; First a 3D model is described with a set of view-based descriptors, each one computed from a different viewpoint. Then a classifier is trained, in a supervised manner, on a collection of 3D models belonging to several shape categories. The classifier learns the set of 2D views that maximize the similarity between shapes of the same class and also the views that discriminate shapes of different classes. Our experiments using the LightField (LFD) descriptors and the Princeton Shape Benchmark demonstrate the performance of the approach and its suitability for classification and online visual browsing of 3D data collections.