Obtaining Generic Parts from Range Images Using a Multi-view Representation

Abstract

We describe a system for obtaining a "generic" parts-based 3D object representation. We use range image data as the input, obtaining a 3D object representation based on 12 geon-like 3D part primitives as the output. The 3D parts-based representation consists of parts detected in the image and their identities. Unlike previous work, we do not make simplifying assumptions such as the availability of perfect line drawings, perfect segmentation, or manual segmentation.We propose a novel method of specifying "generic" 3D parts, i.e., by means of surface adjacency graphs (SAGs). Using the SAGs, we derive an extremely compact multi-view representation of the part primitives, consisting of a total of only 74 views for all 12 primitives. Based on the multi-view representation of parts, we present a method of performing part segmentation from range images, given a good surface segmentation. This method for partsegmentation is more general than common approaches based on Hoffman and Richards′ "principle of transversality." We present two approaches for identifying the parts as one of the 12 3D part primitives. The first approach applies statistical pattern classification methods using parameters estimated by superquadric fitting. Five features derived from the estimated superquadric parameters are used to distinguish between the 12 part primitives. Classification error rates are estimated for k-nearest-neighbor and binary tree classifiers, for real as well as for synthetic range images. The second approach for part identification draws inferences from the distribution of angles between surface normals and the principal axis of a part.We show that intensity data can be used to recover from some misclassifications yielded by the purely range-based methods of part identification. A simple test is applied to check the concavity or convexity of the part silhouette in the intensity image. This serves as a reliable test of whether the part axis is straight orcurved.Results of part segmentation and identification are presented for real range images of several multi-part objects. Our system successfully performs part segmentation and identifies the parts.