Perceptual Evaluation and Metric for Terrain Models

Suren Deepak Rajasekaran, Purdue University

Abstract

The use of Procedural Modeling for the creation of 3D models such as Buildings, Terrains, Trees etc., is becoming increasingly common in Films, Video Games, Urban Modeling and Architectural Visualization. This is due to the primary factor that using procedural models in comparison to traditional hand-modeled models helps in saving time, cost and aids in generation of a larger variety in comparison to a few. However, there are so many open problems in procedural modeling methods that does not rely on any user assistance or aid in generating models especially in terms of their visual quality and perception. Although, it is easy to identify realistic looking models from procedural models, the metrics that make them ’Real’ or ’Procedural’ is still in the indeterminable and remains uncanny in nature. The perceptual metrics (intrinsic factors such as surface features and details, extrinsic factors such as environmental attributes and visual cues) that contributes to the visual perception of Procedural models have not been studied in detail or quantified yet.This dissertation presents a first step in the direction of perceptual evaluation of procedural models of terrains. We gathered and categorized several types of real and synthetic terrains generated by methods used in computer graphics and conducted two large studies with 70 participants ranking them perceptually. The results show that synthetic terrains lack in visual quality and are perceived worse than real terrains with statistical significance. We performed a quantitative study by using localized geomorphology based landform features on terrains (geomorphons) that indicate that valleys, ridges, and hollows have significant perceptual importance. We then used generative deep generative neural network to transfer the features from real terrains to synthetic ones and vice versa to further confirm their importance. A second perceptual experiment with 128 participants confirmed the importance of the transferred features for visual perception. Based on these results, we introduce PTQM (Perceived Terrain Quality Metrics); a novel perceptual metrics based on geomorphons that assigns a number of estimated visual quality of a terrain represented as a digital elevation map. The introduced perceptual metric based on geomorphons indicate that features such as Valley (0.66), Ridge (0.64), Summit (0.44), Depression (0.42), Spur(0.33), and Hollow (0.22) in order have significant perceptual importance. By using linear regression, we show that the presented features are strongly correlated with perceived visual quality.

Degree

Ph.D.

Advisors

Benes, Purdue University.

Subject Area

Geomorphology|Artificial intelligence|Geology

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