Panagiotis D. Ritsos

MEng PhD Essex, FHEA

Lecturer in Visualization

Immersive Environments Lab
Visualization, Data, Modelling and
Graphics (VDMG) research group,

School of Computer Science
and Electronic Engineering,

Bangor University,
Dean Street, Bangor,
Gwynedd, UK, LL57 1UT

Virtual Forestry Generation: Evaluating Models for Tree Placement in Games

Teaser for Virtual Forestry Generation: Evaluating Models for Tree Placement in Games

Abstract

A handful of approaches have been previously proposed to generate procedurally virtual forestry for virtualworlds and computer games, including plant growthmodels and point distribution methods. However, there has been no evaluation to date which assesses how effective these algorithms are at modelling real-world phenomena. In this paper we tackle this issue by evaluating three algorithms used in the generation of virtual forests – a randomly uniform point distribution method (control), a plant competition model, and an iterative random point distribution technique.Our results show that a plant competition model generated more believable content when viewed from an aerial perspective. Interestingly however, we also found that a randomly uniform point distribution method produced forestry which was rated higher in playability and photorealism, when viewed from a first-person perspective. We conclude that the objective of the game designer is important to consider when selecting an algorithm to generate forestry, as the algorithms produce forestry which is perceived differently.

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Citation

B. Williams, P. D. Ritsos, and C. Headleand, “Virtual Forestry Generation: Evaluating Models for Tree Placement in Games,” Computers, vol. 9, no. 1, p. 20, Mar. 2020.  doi:10.3390/computers9010020

Bibtex

@article{Williams-et-al-MDPI-2021,
  author = {Williams, Benjamin and Ritsos, Panagiotis D. and Headleand, Christopher},
  journal = {Computers},
  title = {Virtual Forestry Generation: Evaluating Models for Tree Placement in Games},
  year = {2020},
  publisher = {MDPI},
  volume = {9},
  number = {1},
  pages = {20},
  month = mar,
  doi = {10.3390/computers9010020}
}