Detecting Reflectional Symmetries in 3D Data Through Symmetrical Fitting

Symmetry is ubiquitous in both natural and man-made environments. It reveals redundancies in the structure of the world around us and thus can be used in a variety of visual processing tasks. This paper presents a simple and robust approach to detecting symmetric objects and extracting their symmetries from three-dimensional data. Given a 3D mesh of an object, a set of candidate symmetries are proposed first and are then refined, so that they reflect the complete mesh onto itself. We show how our method can be used to detect symmetric objects in scenes consisting of synthetic 3D models, as well as 3D scans of real environments.

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