publication . Part of book or chapter of book . Contribution for newspaper or weekly magazine . 2018

deep convolutional generative adversarial network for procedural 3d landscape generation based on dem

Wulff-Jensen, Andreas; Rant, Niclas Nerup; Møller, Tobias Nordvig; Billeskov, Jonas Aksel;
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  • Published: 07 Mar 2018
  • Publisher: Springer International Publishing
  • Country: Denmark
Abstract
This paper proposes a novel framework for improving procedural generation of 3D landscapes using machine learning. We utilized a Deep Convolutional Generative Adversarial Network (DC-GAN) to generate heightmaps. The network was trained on a dataset consisting of Digital Elevation Maps (DEM) of the alps. During map generation, the batch size and learning rate were optimized for the most efficient and satisfying map production. The diversity of the final output was tested against Perlin noise using Mean Square Error [1] and Structure Similarity Index [2]. Perlin noise is especially interesting as it has been used to generate game maps in previous productions [3, 4...
Subjects
free text keywords: GAN, Deep Convolutional Generative Adversarial Network, PCG, procedural generated landscapes, Digital Elevation Maps, DEM, heightmaps, games, 3D landscapes, Mean squared error, Digital elevation map, Data mining, computer.software_genre, computer, Generative adversarial network, Usability, business.industry, business, Perlin noise, Machine learning, Computer science, Artificial intelligence, Procedural generation, Heightmap
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Contribution for newspaper or weekly magazine . 2018
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publication . Part of book or chapter of book . Contribution for newspaper or weekly magazine . 2018

deep convolutional generative adversarial network for procedural 3d landscape generation based on dem

Wulff-Jensen, Andreas; Rant, Niclas Nerup; Møller, Tobias Nordvig; Billeskov, Jonas Aksel;