Publications (3)0 Total impact
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ABSTRACT: A common representation in 3-D computer vision is the polygonal surface mesh because meshes can model objects of arbitrary shape and are easily constructed from sensed 3-D data. The resolution of a surface mesh is the overall spacing between vertices that comprise the mesh. Because sensed 3-D points are often unevenly distributed, the resolution of a surface mesh is often poorly defined. We present an algorithm that transforms a mesh with an uneven spacing between vertices into a mesh with a more even spacing between vertices, thus improving its definition of resolution. In addition, we show how the algorithm can be used to control the resolution of surface meshes, making them amenable to multi-resolution approaches in computer vision.
11/1999;
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ABSTRACT: By combining techniques from geometric hashing and structural indexing, we have developed a new representation for recognition of free-form objects from three dimensional data. The representation comprises descriptive spin-images associated with each oriented point on the surface of an object. Constructed using single point bases, spin-images are data level shape descriptions that are used for efficient matching of oriented points. During recognition, scene spin-images are indexed into a stack of model spin-images to establish point correspondences between a model object and scene data. Given oriented point correspondences, a rigid transformation that maps the model into the scene is calculated and then refined and verified using a modified iterative closest point algorithm. Indexing of oriented points bridges the gap between recognition by global properties and feature based recognition without resorting to error-prone segmentation or feature extraction. It requires no kno...
11/1999;
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ABSTRACT: 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 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. This research was performed at Carnegie Mellon University and was supported by the US Department of Energy under contract DE-AC21-92MC29104. 1 1 Introduction Surface matching is a technique from 3-D computer vision that has many applications in the area of robot...
08/1998;
