Representation and Recognition of FreeForm Surfaces

Representation and Recognition of FreeForm Surfaces (1992)

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by M. Hebert , K. Ikeuchi , H. Delingette

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Datum	Value	Source
TITLE	Representation and Recognition of FreeForm Surfaces	INFERENCE
AUTHOR NAME	M. Hebert	SVM HeaderParse 0.2
AUTHOR AFFIL	The Robotics Institute; Carnegie Mellon University,	SVM HeaderParse 0.2
AUTHOR ADDR	5000 Forbes Avenue; Pittsburgh PA 15213	SVM HeaderParse 0.2
AUTHOR NAME	K. Ikeuchi	SVM HeaderParse 0.2
AUTHOR NAME	H. Delingette	SVM HeaderParse 0.2
AUTHOR AFFIL	the Avionics Laboratory, Wright-Patterson AFB, under F33615-90-C-1465 and F33615-93-1-1282.; 2. This research was conducted while H. Delingette was a visiting scholar at the Robotics Institute. His permanent	SVM HeaderParse 0.2
AUTHOR ADDR	address is now: INRIA, Groupe Epidaure, 2004 Route Des Lucioles, BP 93, 06902 Sophia-Antipolis,	SVM HeaderParse 0.2
ABSTRACT	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 pose-invariant 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 three-dimensional 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.	SVM HeaderParse 0.2
YEAR	1992	INFERENCE
VENUE TYPE	TECHREPORT	INFERENCE
TECH	Technical Report CMU-CS-92-124	INFERENCE
CITATIONS	27 found	ParsCit 1.0