Non-Rigid 3D Shape Retrieval via Large Margin Nearest Neighbor Embedding

Abstract

In this paper, we propose a highly efficient metric learning approach to non-rigid 3D shape analysis. From a training set of 3D shapes from different classes, we learn a transformation of the shapes which optimally enforces a clustering of shapes from the same class. In contrast to existing approaches, we do not perform a transformation of individual local point descriptors, but a linear embedding of the entire distribution of shape descriptors. It turns out that this embedding of the input shapes is sufficiently powerful to enable state of the art retrieval performance using a simple nearest neighbor classifier. We demonstrate experimentally that our approach substantially outperforms the state of the art non-rigid 3D shape retrieval methods on the recent benchmark data set SHREC’14 Non-Rigid 3D Human Models, both in classification accuracy and runtime.