Browsing Rendering 2020 - DL-only Track by Title

Now showing items 1-6 of 6

    • Joint SVBRDF Recovery and Synthesis From a Single Image using an Unsupervised Generative Adversarial Network 

      Zhao, Yezi; Wang, Beibei; Xu, Yanning; Zeng, Zheng; Wang, Lu; Holzschuch, Nicolas (The Eurographics Association, 2020)
      We want to recreate spatially-varying bi-directional reflectance distribution functions (SVBRDFs) from a single image. Pro- ducing these SVBRDFs from single images will allow designers to incorporate many new materials in ...

    • Multi-Scale Appearance Modeling of Granular Materials with Continuously Varying Grain Properties 

      Zhang, Cheng; Zhao, Shuang (The Eurographics Association, 2020)
      Many real-world materials such as sand, snow, salt, and rice are comprised of large collections of grains. Previously, multiscale rendering of granular materials requires precomputing light transport per grain and has ...

    • Real-time Monte Carlo Denoising with the Neural Bilateral Grid 

      Meng, Xiaoxu; Zheng, Quan; Varshney, Amitabh; Singh, Gurprit; Zwicker, Matthias (The Eurographics Association, 2020)
      Real-time denoising for Monte Carlo rendering remains a critical challenge with regard to the demanding requirements of both high fidelity and low computation time. In this paper, we propose a novel and practical deep ...

    • Rendering 2020 DL Track: Frontmatter 

      Dachsbacher, Carsten; Pharr, Matt (The Eurographics Association, 2020)

    • Temporal Normal Distribution Functions 

      Tessari, Lorenzo; Hanika, Johannes; Dachsbacher, Carsten; Droske, Marc (The Eurographics Association, 2020)
      Specular aliasing can make seemingly simple scenes notoriously hard to render efficiently: small geometric features with high curvature and near specular reflectance result in tiny lighting features which are difficult to ...

    • Temporal Sample Reuse for Next Event Estimation and Path Guiding for Real-Time Path Tracing 

      Dittebrandt, Addis; Hanika, Johannes; Dachsbacher, Carsten (The Eurographics Association, 2020)
      Good importance sampling is crucial for real-time path tracing where only low sample budgets are possible. We present two efficient sampling techniques tailored for massively-parallel GPU path tracing which improve next ...