PTRM: Perceived Terrain Realism Metric

PTRM: Metrika vnímaného realismu modelů terénů

journal article

English

Rajasekaran Suren Deepak
Kang Hao
Čadík Martin, doc. Ing., Ph.D. (DCGM)
Galin Eric
Guérin Eric
Peytavie Adrien
Slavík Pavel, prof. Ing., CSc. (CM-SFE)
Beneš Bedřich

• https://dl.acm.org/doi/10.1145/3514244

Procedural modeling, terrains, visual perception, feature transfer, neural
networks

Terrains are visually prominent and commonly needed objects in many computer
graphics applications. While there are many algorithms for synthetic terrain
generation, it is rather difficult to assess the realism of a generated output.
This paper presents a first step towards the direction of perceptual evaluation
for terrain models. We gathered and categorized several classes of real terrains,
and we generated synthetic terrain models using computer graphics methods. The
terrain geometries were rendered by using the same texturing, lighting, and
camera position. Two studies on these image sets were conducted, ranking the
terrains perceptually, and showing that the synthetic terrains are perceived as
lacking realism compared to the real ones. 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 has a significant perceptual effect. The importance
and presence of the terrain features were confirmed by using a generative deep
neural network that transferred the features between the geometric models of the
real terrains and the synthetic ones. The feature transfer was followed by
another perceptual experiment that further showed their importance and effect on
perceived realism. We then introduce Perceived Terrain Realism Metrics (PTRM)
that estimates human perceived realism of a terrain represented as a digital
elevation map by relating distribution of terrain features with their perceived
realism. This metric can be used on a synthetic terrain, and it will output an
estimated level of perceived realism. We validated the proposed metrics on real
and synthetic data and compared them to the perceptual studies.

2022

1–22

ACM Transactions on Applied Perception, vol. 19, no. 2, ISSN 1544-3558

10.1145/3514244

000827414800002

2-s2.0-85134876377