Shape Measure for Identifying Perceptually Informative Parts of 3D Objects

We propose a mathematical approach for quantifying shape complexity of 3D surfaces based on perceptual principles of visual saliency. Our curvature variation measure (CVM), as a 3D feature, combines surface curvature and information theory by leveraging bandwidth-optimized kernel density estimators. Using a part decomposition algorithm for digitized 3D objects, represented as triangle meshes, we apply our shape measure to transform the low level mesh representation into a perceptually informative form. Further, we analyze the effects of noise, sensitivity to digitization, occlusions, and descriptiveness to demonstrate our shape measure on laser-scanned real world 3D objects.

• Type: Conference
• Year: 2006
• Where: 3DPVT
• Authors: Sreenivas R. Sukumar, David Page, Andrei V. Gribok, Andreas Koschan, Mongi A. Abidi
• Keywords: 3DPVT 2006, Applied Computing, Combines Surface Curvature, Curvature Variation Measure, Shape Measure