Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/129082
DC FieldValue
dc.titleAn Approach to 3D Pose Determination
dc.contributor.authorEzquerra, N.
dc.contributor.authorMullick, R.
dc.date.accessioned2016-10-26T11:01:48Z
dc.date.available2016-10-26T11:01:48Z
dc.date.issued1996-04
dc.identifier.citationEzquerra, N.,Mullick, R. (1996-04). An Approach to 3D Pose Determination. ACM Transactions on Graphics 15 (2) : 99-120. ScholarBank@NUS Repository.
dc.identifier.issn07300301
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/129082
dc.description.abstractThe orientation, or pose, of an object is a fundamental property that helps to define the geometrical relationship between the object and its environment. In addition, knowledge of object orientation can also facilitate interpretive and decision-making tasks in a variety of practical domains, including industrial, meteorological, and medical applications. Determining object pose, however, remains an open research question in the fields of graphics and visualization. This article describes a novel yet intuitively simple approach, which we call topological goniometry, to directly determine the pose of a three-dimensional object from 3D data. The topology of interest is that of two-sided surfaces in a three-manifold, and includes objects whose shapes are unaffected by elastic transformations. Algorithmically, topological goniometry is composed of the following major steps. The first analyzes the global topology in order to generate a distribution of 3D coordinate triplets in the proximity of the desired pose axis. Using this set of 3D points, the second step then invokes a "3D Walk" algorithm that considers the local topology to produce a generalized curve representing an estimate of the object's axis of pose. The resultant pose axis is thus not constrained to lie along a straight line but can be a generalized 3D curve. The methods are illustrated with a variety of synthetically created models that exhibit duct-like shapes, and are further tested by introducing noise as well as deformations to these models. The approach is also applied to a number of real discrete data obtained from meteorological and medical domains. The results suggest that the approach is applicable to both real and synthetic datasets and is shown to be robust, computationally efficient, and applicable to a variety of problems. The approach can incorporate context- or application-dependent information about the object of interest by using a set of constraints that guide the process of orientation determination. This article describes the approach, its implementation, and the results obtained with numerous applications.
dc.sourceScopus
dc.subjectAlgorithms
dc.subjectAxis of orientation
dc.subjectDesign
dc.subjectGeometry
dc.subjectI.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism - object presentation, object modeling
dc.subjectOrientation determination
dc.subjectPose determination
dc.subjectTheory
dc.typeArticle
dc.contributor.departmentCENTRE FOR INFORMATION ENHANCED MEDICINE
dc.description.sourcetitleACM Transactions on Graphics
dc.description.volume15
dc.description.issue2
dc.description.page99-120
dc.description.codenATGRD
dc.identifier.isiutNOT_IN_WOS
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