Efficient Multiple Model Recognition in Cluttered 3-D Scenes

We present a 3-D shape-based object recognition system for simultaneous recognition of multiple objects in scenes containing clutter and occlusion. Recognition is based on matching sugaces 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 su$ace meshes. We present a compression scheme for spinimages that results in eficient 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.

• Type: Conference
• Year: 1998
• Where: CVPR
• Authors: Andrew Edie Johnson, Martial Hebert
• Keywords: Computer Vision, CVPR 1998, Multiple Object Recognition, Recognition Trials, Scenes Containing Clutter, Shape-based Object Recognition, Simultaneous Recognition