Publikationen

Autor	Zhang, Alex; Chen, Kan; Johan, Henry; Erdt, Marius
Datum	2022
Art	Journal Article
Abstrakt	We propose a high-performance texture streaming system for real-time rendering of large 3D cities with millions of textures. Our main contribution is a texture streaming system that automatically adjusts the streaming workload at runtime based on measured frame latencies, specifically addressing the high memory binding costs of hardware virtual texturing which causes frame rate stuttering. Our system streams textures in parallel with prioritization based on GPU computed mesh perceptibility, and these textures are cached in a sparse partially resident image at runtime without the need for a texture preprocessing step. In addition, we improve rendering quality by minimizing texture pop-in artifacts using a color blending scheme based on mipmap levels. We evaluate our texture streaming system using three structurally distinct datasets with many textures and compared it to a baseline, a game engine, and our prior method. Results show an 8X improvement in rendering performance and 7X improvement in rendering quality compared to the baseline.
Url	https://publica.fraunhofer.de/handle/publica/267803

High-performance Adaptive Texture Streaming and Rendering of Large 3D Cities