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Defining the Pose of Any 3D Rigid Object and an Associated Distance

Brégier, Romain ; Devernay, Frédéric ; Leyrit, Laetitia ; Crowley, James L.

International journal of computer vision, 2018-06, Vol.126 (6), p.571-596 [Periódico revisado por pares]

New York: Springer US

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Título:
Defining the Pose of Any 3D Rigid Object and an Associated Distance
Autor: Brégier, Romain ; Devernay, Frédéric ; Leyrit, Laetitia ; Crowley, James L.
Assuntos: Artificial Intelligence ; Computer Imaging ; Computer Science ; Computer Vision and Pattern Recognition ; Euclidean geometry ; Euclidean space ; Image Processing and Computer Vision ; Image processing systems ; Mathematical Physics ; Mathematics ; Mechanics ; Metric Geometry ; Pattern Recognition ; Pattern Recognition and Graphics ; Physics ; Searching ; Solid mechanics ; Transformations ; Vision ; Vision systems
É parte de: International journal of computer vision, 2018-06, Vol.126 (6), p.571-596
Descrição: The pose of a rigid object is usually regarded as a rigid transformation, described by a translation and a rotation. However, equating the pose space with the space of rigid transformations is in general abusive, as it does not account for objects with proper symmetries—which are common among man-made objects. In this article, we define pose as a distinguishable static state of an object, and equate a pose to a set of rigid transformations. Based solely on geometric considerations, we propose a frame-invariant metric on the space of possible poses, valid for any physical rigid object, and requiring no arbitrary tuning. This distance can be evaluated efficiently using a representation of poses within a Euclidean space of at most 12 dimensions depending on the object’s symmetries. This makes it possible to efficiently perform neighborhood queries such as radius searches or k-nearest neighbor searches within a large set of poses using off-the-shelf methods. Pose averaging considering this metric can similarly be performed easily, using a projection function from the Euclidean space onto the pose space. The practical value of those theoretical developments is illustrated with an application of pose estimation of instances of a 3D rigid object given an input depth map, via a Mean Shift procedure.
Editor: New York: Springer US
Idioma: Inglês

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Defining the Pose of Any 3D Rigid Object and an Associated Distance

Brégier, Romain ; Devernay, Frédéric ; Leyrit, Laetitia ; Crowley, James L.

New York: Springer US

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