Abstract
Implicit shape parameterization for Kansei design is a procedure that use 3D-models, or concepts, to span a shape space for surfaces in the automotive field. A low-dimensional, yet accurate shape descriptor was found by Principal Component Analysis of an ensemble of point-clouds, which were extracted from mesh-based surfaces modeled in a CAD-program. A theoretical background of the procedure is given along with step-by-step instructions for the required data-processing. The results show that complex surfaces can be described very efficiently, and encode design features by an implicit approach that does not rely on error-prone explicit parameterizations. This provides a very intuitive way to explore shapes for a designer, because various design features can simply be introduced by adding new concepts to the ensemble. Complex shapes have been difficult to analyze with Kansei methods due to the large number of parameters involved, but implicit parameterization of design features provides a low-dimensional shape descriptor for efficient data collection, model-building and analysis of emotional content in 3D-surfaces.
