We introduce a method for estimation of rotation invariant local shape descriptors for 3D models. This method follows a successful idea commonly used to obtain rotation invariant descriptors in 2D images, and improves it by tackling the difficulty of the 3 degrees of freedom that exists in 3D models. Our method is simple, yet it achieves high levels of invariance after rotation transformations, and it produces short descriptors that can be efficiently used in several tasks. Such is the case of automatic classification of 3D surfaces with archaeological value, in which the proposed method attains state-of-the-art results in shorter times when compared with previous methods.
CITATION STYLE
Roman-Rangel, E., Jimenez-Badillo, D., & Marchand-Maillet, S. (2016). Rotation invariant local shape descriptors for classification of archaeological 3D models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9703, pp. 13–22). Springer Verlag. https://doi.org/10.1007/978-3-319-39393-3_2
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