3D Symmetry Detection Using The Extended Gaussian Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Symmetry Identification of a 3-D Object Represented by Octree
IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
SMI '04 Proceedings of the Shape Modeling International 2004
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Folding meshes: hierarchical mesh segmentation based on planar symmetry
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SymPan: 3D model pose normalization via panoramic views and reflective symmetry
3DOR '13 Proceedings of the Sixth Eurographics Workshop on 3D Object Retrieval
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In this paper we present a new method for alignment of 3D models. This approach is based on two types of symmetries of the models: the reflective symmetry and the local translational symmetry along a direction. Inspired by the work on the principal component analysis (PCA), we select the best optimal alignment axes within the PCA-axes, the plane reflection symmetry being used as a selection criterion. This pre-processing transforms the alignment problem into an indexing scheme based on the number of the retained PCA-axes. In order to capture the local translational symmetry of a shape along a direction, we introduce a new measure we call the local translational invariance cost (LTIC). The mirror planes of a model are also used to reduce the number of candidate coordinate frames when looking for the one which corresponds to the user's perception. Experimental results show that the proposed method finds the rotation that best aligns a 3D mesh.