Modeling Real Objects for Kansei-based Shape Retrieval

A large number of 3D models are created on computers and available for networks.Some content-based retrieval technologies are indispensable to find out particular data from such anonymous datasets.Though several shape retrieval technologies have been developed,little attention has been given to the points on humans sense and impression (as known as Kansei) in the conventional techniques.In this paper,the authors propose a novel method of shape retrieval based on shape impression of humans Kansei.The key to the method is the Gaussian curvature distribution from 3D models as features for shape retrieval.Then it classifies the 3D models by extracted feature and measures similarity among models in storage.