A Non-cooperative Game for 3D Object Recognition in Cluttered Scenes

Abstract—During the last few years a wide range of algorithms and devices have been made available to easily acquire range images. To this extent, the increasing abundance of depth data boosts the need for reliable and unsupervised analysis techniques, spanning from part registration to automated segmentation. In this context, we focus on the recognition of known objects in cluttered and incomplete 3D scans. Fitting a model to a scene is a very important task in many scenarios such as industrial inspection, scene understanding and even gaming. For this reason, this problem has been extensively tackled in literature. Nevertheless, while many descriptor-based approaches have been proposed, a number of hurdles still hinder the use of global techniques. In this paper we try to offer a different perspective on the topic. Specifically, we adopt an evolutionary selection algorithm in order to extend the scope of local descriptors to satisfy global pairwise constraints. In addition, the ver...

• Type: Journal
• Year: 2011
• Where: 3DIM
• Authors: Andrea Albarelli, Emanuele Rodolà, Filippo Bergamasco, Andrea Torsello
• Keywords: 3DIM 2011, Evolutionary Selection, Global Techniques, Image Processing, Selection Algorithm