Evaluation of supra-threshold perceptual metrics for 3D models

Abstract

Measures of dissimilarity of 3D models are necessary in a wide range of applications such as geometry compression, simplification, and 3D model retrieval. In many cases a metric that models perceptual dissimilarity is desirable. Recently, metrics for 3D models have been evaluated in that respect using concepts such as just noticeable differences, rankings, and others. We propose a simple experimental setup for evaluating supra-threshold perception of 3D models in which users select models at equal perceptual distance to given pairs of models. We discuss the advantages of our approach and report the results of a field study comparing six objective distance measures applied to palettes of simplified reference models. We found that the objective measures are biased, and generally imagebased metrics perform better than metrics based on the original 3D geometry.