A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Iterative point matching for registration of free-form curves and surfaces
International Journal of Computer Vision
Interior/exterior classification of polygonal models
Proceedings of the conference on Visualization '00
Automatic View Selection in Multi-View Object Recognition
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Rotation invariant spherical harmonic representation of 3D shape descriptors
Proceedings of the 2003 Eurographics/ACM SIGGRAPH symposium on Geometry processing
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Detecting image orientation based on low-level visual content
Computer Vision and Image Understanding
A Survey of Content Based 3D Shape Retrieval Methods
SMI '04 Proceedings of the Shape Modeling International 2004
SMI '04 Proceedings of the Shape Modeling International 2004
ACM SIGGRAPH 2005 Papers
Detector of image orientation based on Borda Count
Pattern Recognition Letters
A planar-reflective symmetry transform for 3D shapes
ACM SIGGRAPH 2006 Papers
Partial and approximate symmetry detection for 3D geometry
ACM SIGGRAPH 2006 Papers
Character Recognition Systems: A Guide for Students and Practitioners
Character Recognition Systems: A Guide for Students and Practitioners
Upright orientation of man-made objects
ACM SIGGRAPH 2008 papers
Face recognition across pose: A review
Pattern Recognition
Three-dimensional shape searching: state-of-the-art review and future trends
Computer-Aided Design
Tensor Decompositions and Applications
SIAM Review
TILT: transform invariant low-rank textures
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Active co-analysis of a set of shapes
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
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Most man-made models can be posed at a unique upright orientation which is consistent to human sense. However, since produced by various techniques, digital man-made models, such as polygon meshes, might be sloped far from the upright orientation. We present a novel unsupervised approach for finding the upright orientation of man-made models by using a low-rank matrix theorem based technique. We propose that projections of the models could be regarded as low-rank matrices when they have been posed at axis-aligned orientations. The models are to be iteratively rotated by using the recently presented TILT technique, in order to ensure that their projections have optimal low-rank observations. After that, the upright orientation can be easily picked up from the six axis-aligned candidate orientations by analysis on geometric properties of the model. The approach does not require any other training set of models and should be regardless of the model quality. A number of experiments will be shown to illustrate the effectiveness and robustness of the proposed approach.