Level-of-Detail Streaming and Rendering using Bidirectional Sparse Virtual Texture Functions

Abstract

Bidirectional Texture Functions (BTFs) are among the highest quality material representations available today and thus well suited whenever an exact reproduction of the appearance of a material or complete object is required. In recent years, BTFs have started to find application in various industrial settings and there is also a growing interest in the cultural heritage domain. BTFs are usually measured from real-world samples and easily consist of tens or hundreds of gigabytes. By using data-driven compression schemes, such as matrix or tensor factorization, a more compact but still faithful representation can be derived. This way, BTFs can be employed for real-time rendering of photo-realistic materials on the GPU. However, scenes containing multiple BTFs or even single objects with high-resolution BTFs easily exceed available GPU memory on today's consumer graphics cards unless quality is drastically reduced by the compression. In this paper, we propose the Bidirectional Sparse Virtual Texture Function, a hierarchical level-of-detail approach for the real-time rendering of large BTFs that requires only a small amount of GPU memory. More importantly, for larger numbers or higher resolutions, the GPU and CPU memory demand grows only marginally and the GPU workload remains constant. For this, we extend the concept of sparse virtual textures by choosing an appropriate prioritization, finding a trade off between factorization components and spatial resolution. Besides GPU memory, the high demand on bandwidth poses a serious limitation for the deployment of conventional BTFs. We show that our proposed representation can be combined with an additional transmission compression and then be employed for streaming the BTF data to the GPU from from local storage media or over the Internet. In combination with the introduced prioritization this allows for the fast visualization of relevant content in the users field of view and a consecutive progressive refinement.