Results 1 
2 of
2
Representation and Recognition of FreeForm Surfaces
, 1992
"... We introduce a new surface representation for recognizing curved objects. Our approach begins by representing an object by a discrete mesh of points built from range data or from a geometric model of the object. The mesh is computed from the data by deforming a standard shaped mesh, for example, an ..."
Abstract

Cited by 52 (6 self)
 Add to MetaCart
We introduce a new surface representation for recognizing curved objects. Our approach begins by representing an object by a discrete mesh of points built from range data or from a geometric model of the object. The mesh is computed from the data by deforming a standard shaped mesh, for example, an ellipsoid, until it fits the surface of the object. We define local regularity constraints that the mesh must satisfy. We then define a canonical mapping between the mesh describing the object and a standard spherical mesh. A surface curvature index that is poseinvariant is stored at every node of the mesh. We use this object representation for recognition by comparing the spherical model of a reference object with the model extracted from a new observed scene. We show how the similarity between reference model and observed data can be evaluated and we show how the pose of the reference object in the observed scene can be easily computed using this representation. We present results on real range images which show that this approach to modelling and recognizing threedimensional objects has three main advantages: First, it is applicable to complex curved surfaces that cannot be handled by conventional techniques. Second, it reduces the recognition problem to the computation of similarity between spherical distributions; in particular, the recognition algorithm does not require any combinatorial search. Finally, even though it is based on a spherical mapping, the approach can handle occlusions and partial views.
Surface Approximation of Complex 3D Objects
, 1994
"... Introduction Range sensing is a mature technology, and there are many methods, such as time of flight and MRI, collect 3D data based on this technology. In addition to this, 3D data can also be obtained in passive ways like stereo and shape from X methods. The data obtained from the above sources i ..."
Abstract
 Add to MetaCart
Introduction Range sensing is a mature technology, and there are many methods, such as time of flight and MRI, collect 3D data based on this technology. In addition to this, 3D data can also be obtained in passive ways like stereo and shape from X methods. The data obtained from the above sources is in the form of points. But, in computer vision, what we need are some properties such as the curvature, normal, and principal directions. These quantities relate to the underlying surface, which is not made explicit in the original data. Furthermore, it is even more difficult if some ordering relation among the data points is not known. This happens mainly when we gather data points from various sources. Analytical surface construction of a cloud of points (boundary points of the object) becomes important because it is much easier to extract the features from an analytical surfaces. So, we need some tools to construct an analytical description (for example, surface) for the collect