Using spin images for efficient object recognition in cluttered 3D scenes

Citations Over TimeTop 1% of 1999 papers

Abstract

We present a 3D shape-based object recognition system for simultaneous recognition of multiple objects in scenes containing clutter and occlusion. Recognition is based on matching surfaces by matching points using the spin image representation. The spin image is a data level shape descriptor that is used to match surfaces represented as surface meshes. We present a compression scheme for spin images that results in efficient multiple object recognition which we verify with results showing the simultaneous recognition of multiple objects from a library of 20 models. Furthermore, we demonstrate the robust performance of recognition in the presence of clutter and occlusion through analysis of recognition trials on 100 scenes.

Related Papers

• → Large-scale tests of a keyed, appearance-based 3-D object recognition system(1998)59 cited
• Three-Dimensional Object Recognition Systems(1993)
• The Matching Algorithm using Block Segmentation for Object Recognition(2008)
• → From Local Features to Global Shape Constraints: Heterogeneous Matching Scheme for Recognizing Objects under Serious Background Clutter(2011)4 cited
• → 3D Object Recognition Based on Machine Vision(2012)