Deep representation of a normal map for screen-space fluid rendering

Myungjin Choi, Jee Hyeok Park, Qimeng Zhang, Byeung Sun Hong, Chang Hun Kim

    Research output: Contribution to journalArticlepeer-review

    2 Citations (Scopus)

    Abstract

    We propose a novel method for addressing the problem of efficiently generating a highly refined normal map for screen-space fluid rendering. Because the process of filtering the normal map is crucially important to ensure the quality of the final screen-space fluid rendering, we employ a conditional generative adversarial network (cGAN) as a filter that learns a deep normal map representation, thereby refining the low-quality normal map. In particular, we have designed a novel loss function dedicated to refining the normal map information, and we use a specific set of auxiliary features to train the cGAN generator to learn features that are more robust with respect to edge details. Additionally, we constructed a dataset of six different typical scenes to enable effective demonstrations of multitype fluid simulation. Experiments indicated that our generator was able to infer clearer and more detailed features for this dataset than a basic screen-space fluid rendering method. Moreover, in some cases, the results generated by our method were even smoother than those generated by the conventional surface reconstruction method. Our method improves the fluid rendering results via the high-quality normal map while preserving the advantages of the screen-space fluid rendering methods and the traditional surface reconstruction methods, including that of the computation time being independent of the number of simulation particles and the spatial resolution being related only to image resolution.

    Original languageEnglish
    Article number9065
    JournalApplied Sciences (Switzerland)
    Volume11
    Issue number19
    DOIs
    Publication statusPublished - 2021 Oct 1

    Bibliographical note

    Publisher Copyright:
    © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

    Keywords

    • Fluid rendering
    • Image-based rendering
    • Machine learning
    • Screen space rendering
    • Supervised learning

    ASJC Scopus subject areas

    • General Materials Science
    • Instrumentation
    • General Engineering
    • Process Chemistry and Technology
    • Computer Science Applications
    • Fluid Flow and Transfer Processes

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