PTRM: Perceived Terrain Realism Metrics

TL;DR

This paper introduces PTRM, a perceptual metric for evaluating terrain realism based on geomorphological features, validated through perceptual studies comparing real and synthetic terrains.

Contribution

It presents the first perceptual evaluation framework for terrain realism and introduces PTRM, linking terrain features to perceived realism with validation through perceptual experiments.

Findings

Synthetic terrains are perceived as less realistic than real terrains.

Certain geomorphological features significantly influence perceived terrain realism.

PTRM effectively estimates terrain realism based on feature distribution.

Abstract

Terrains are visually important and commonly used in computer graphics. While many algorithms for their generation exist, it is difficult to assess the realism of a generated terrain. This paper presents a first step in the direction of perceptual evaluation of terrain models. We gathered and categorized several classes of real terrains and we generated synthetic terrains by using methods from computer graphics. We then conducted two large studies ranking the terrains perceptually and showing that the synthetic terrains are perceived as lacking realism as compared to the real ones. Then we provide insight into the features that affect the perceived realism by a quantitative evaluation based on localized geomorphology-based landform features (geomorphons) that categorize terrain structures such as valleys, ridges, hollows, etc. We show that the presence or absence of certain features have a significant perceptual effect. We then introduce Perceived Terrain Realism Metrics (PTRM); a perceptual metrics that estimates perceived realism of a terrain represented as a digital elevation map by relating distribution of terrain features with their perceived realism. We validated PTRM on real and synthetic data and compared it to the perceptual studies. To confirm the importance of the presence of these features, we used a generative deep neural network to transfer them between real terrains and synthetic ones and we performed another perceptual experiment that further confirmed their importance for perceived realism.