KAUST DepartmentComputer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Visual Computing Center (VCC)
Permanent link to this recordhttp://hdl.handle.net/10754/625254
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AbstractFinding similar points in globally or locally similar shapes has been studied extensively through the use of various point descriptors or shape-matching methods. However, little work exists on finding similar points in dissimilar shapes. In this paper, we present the results of a study where users were given two dissimilar two-dimensional shapes and asked to map a given point in the first shape to the point in the second shape they consider most similar. We find that user mappings in this study correlate strongly with simple geometric relationships between points and shapes. To predict the probability distribution of user mappings between any pair of simple two-dimensional shapes, two distinct statistical models are defined using these relationships. We perform a thorough validation of the accuracy of these predictions and compare our models qualitatively and quantitatively to well-known shape-matching methods. Using our predictive models, we propose an approach to map objects or procedural content between different shapes in different design scenarios.
CitationHecher M, Guerrero P, Wonka P, Wimmer M (2017) How Do Users Map Points Between Dissimilar Shapes? IEEE Transactions on Visualization and Computer Graphics: 1–1. Available: http://dx.doi.org/10.1109/TVCG.2017.2730877.
SponsorsThis research was partially financed by the Austrian Science Fund project Nr. FWF P24600-N23.