44-Issue 4

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/3607198

Browse

Browsing 44-Issue 4 by Subject "CCS Concepts: Computing methodologies → Rendering"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Real-time Level-of-detail Strand-based Rendering
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Huang, Tao; Zhou, Yang; Lin, Daqi; Zhu, Junqiu; Yan, Ling-Qi; Wu, Kui; Wang, Beibei; Wilkie, Alexander
    We present a real-time strand-based rendering framework that ensures seamless transitions between different level-of-detail (LoD) while maintaining a consistent appearance. We first introduce an aggregated BCSDF model to accurately capture both single and multiple scattering within the cluster for hairs and fibers. Building upon this, we further introduce a LoD framework for hair rendering that dynamically, adaptively, and independently replaces clusters of individual hairs with thick strands based on their projected screen widths. Through tests on diverse hairstyles with various hair colors and animation, as well as knit patches, our framework closely replicates the appearance of multiple-scattered full geometries at various viewing distances, achieving up to a 13× speedup.
  • Thumbnail Image
    Item
    Wavelet Representation and Sampling of Complex Luminaires
    (The Eurographics Association and John Wiley & Sons Ltd., 2025) Atanasov, Asen; Koylazov, Vladimir; Wang, Beibei; Wilkie, Alexander
    We contribute a technique for rendering the illumination of complex luminaires based on wavelet-compressed light fields while the direct appearance of the luminaire is handled with previous techniques. During a brief photon tracing phase, we precompute the radiance field of the luminaire. Then, we employ a compression scheme which is designed to facilitate fast per-ray run-time reconstructions of the field and importance sampling. To treat aliasing, we propose a two-component filtering solution: a 4D Gaussian filter during the pre-computation stage and a 4D stochastic Gaussian filter during rendering. We have developed an importance sampling strategy based on providing an initial guess from low-resolution and low-memory viewpoint samplers that is subsequently refined by a hierarchical process over the wavelet frequency bands. Our technique is straightforward to integrate in rendering systems and has all the features that make it practical for production renderers - MIS compatibility, brief pre-computation, low memory requirements, and efficient field evaluation and importance sampling.