Fast calculation of large-scale global illumination optimized by multi-level storage strategy

doi:10.13474/j.cnki.11-2246.2020.0079

Abstract

Abstract: Aiming at the problem of large amount of global illumination calculation for large-scale complex scene rendering, an improved voxel cone tracking global illumination algorithm based on multi-level resolution texture storage structure is proposed. Firstly, a multi-level texture storage structure based on human visual characteristics to efficiently store lighting information is used. And then, in the direct lighting calculation, combined with the characteristics of the storage result, a combination of GPU hardware acceleration of hybrid storage and software acceleration of discarded uncontributed nodes is used to further improve lighting rendering efficiency. Finally, the cone wave filter is improved based on the multi-level texture and the adaptive combination of closed body nodes to achieve efficient calculation of global illumination. The experimental results show that, the improved algorithm reduces the amount of lighting calculations, reduces the system memory footprint, and achieves a scene lighting rendering effect similar to the classic algorithm, thereby verifying its effectiveness in large-scale lighting calculations.

Key words: large-scale complex scene rendering, global illumination calculation, multi-level texture storage, voxel cone tracking algorithm, node adaptive merge

CLC Number:

P208

QI Shuang, YU Guangji. Fast calculation of large-scale global illumination optimized by multi-level storage strategy[J]. Bulletin of Surveying and Mapping, 2020, 0(3): 64-68.

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