“OceanGAN: A Deep Learning Alternative to Physics-Based Ocean Rendering” by Ratto, Szeto, Slocum and Del Bene

  • ©Christopher Ratto, Mimi Szeto, David Slocum, and Kevin Del Bene

  • ©Christopher Ratto, Mimi Szeto, David Slocum, and Kevin Del Bene

  • ©Christopher Ratto, Mimi Szeto, David Slocum, and Kevin Del Bene



Entry Number: 89


    OceanGAN: A Deep Learning Alternative to Physics-Based Ocean Rendering



    Physics-based models for ocean dynamics and optical raytracing are used extensively for rendering maritime scenes in computer graphics [Darles et al. 2011]. Raytracing models can provide high fidelity representations of an ocean image with full control of the underlying environmental conditions, sensor specifications, and viewing geometry. However, the computational expense of rendering ocean scenes can be high. This work demonstrates an alternative approach to ocean raytracing via machine learning, specifically Generative Adversarial Networks (GANs) [Goodfellow et al. 2014]. In this paper, we demonstrate that a GAN trained on several thousand small scenes produced by a raytracing model can be used to generate megapixel scenes roughly an order of magnitude faster with a consistent wave spectrum and minimal processing artifacts.


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©Christopher Ratto, Mimi Szeto, David Slocum, and Kevin Del Bene


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