Perceptual Metrics for Static and Dynamic Triangle Meshes

Abstract

Almost all mesh processing procedures cause some more or less visible changes in the appearance of objects represented by polygonal meshes. In many cases, such as mesh watermarking, simplification or lossy compression, the objective is to make the change in appearance negligible, or as small as possible, given some other constraints. Measuring the amount of distortion requires taking into account the final purpose of the data. In many applications, the final consumer of the data is a human observer, and therefore the perceptibility of the introduced appearance change by a human observer should be the criterion that is taken into account when designing and configuring the processing algorithms. In this review, we discuss the existing comparison metrics for static and dynamic (animated) triangle meshes. We describe the concepts used in perception-oriented metrics used for 2D image comparison, and we show how these concepts are employed in existing 3D mesh metrics. We describe the character of subjective data used for evaluation of mesh metrics and provide comparison results identifying the advantages and drawbacks of each method. Finally, we also discuss employing the perception-correlated metrics in perception-oriented mesh processing algorithms.

A perceptual quality metric for dynamic triangle meshes

“…Image-based solutions, on the other hand, work in 2D image space, and use rendered images to estimate the quality of the given mesh. Several quality metrics have been proposed; [6], [12], and [28] present surveys on the recently proposed 3D quality metrics.…”

“…The metric is based on the idea that perception of distortion is related to local and relative changes rather than global and absolute changes [12]. The spatial part of the error metric is obtained by computing the standard deviation of relative edge lengths within a topological neighborhood of each vertex.…”

Visual saliency guided textured model simplification

“…Common approaches include minimising local surface distortion based on error metrics [10], preserving salient regions that stand out from their surrounding context [20], and applying perceptual principles [6]. However, texture maps are popular to involve in object modelling for the sake of improving realism, taking the responsibility for modelling most of the object details and letting mesh geometry focus on modelling object shapes.…”

A Curvature Tensor Distance for Mesh Visual Quality Assessment

“…It is common that 3D meshes undergo some lossy operations like simplification, compression and watermarking. Since the end users are often human beings, it is thus important to derive metrics that can faithfully evaluate the perceptual distortions introduced by such operations [1]. Classical metrics of simple geometric distances (e.g.…”

“…Classical metrics of simple geometric distances (e.g. root mean squared error and Hausdorff distance) [2,3] have been demonstrated not relevant to human visual perception and thus fail to predict the visual difference between a pair of reference and deformed meshes [1].…”