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Hierarchical Raster Occlusion Culling
Author(s) -
Lee Gi Beom,
Jeong Moonsoo,
Seok Yechan,
Lee Sungkil
Publication year - 2021
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.142649
Subject(s) - culling , computer science , raster graphics , scalability , tree traversal , raster data , occlusion , computer graphics (images) , algorithm , database , medicine , herd , veterinary medicine , cardiology
This paper presents a scalable online occlusion culling algorithm, which significantly improves the previous raster occlusion culling using object‐level bounding volume hierarchy. Given occluders found with temporal coherence, we find and rasterize coarse groups of potential occludees in the hierarchy. Within the rasterized bounds, per‐pixel ray casting tests fine‐grained visibilities of every individual occludees. We further propose acceleration techniques including the read‐back of counters for tightly‐packed multidrawing and occluder filtering. Our solution requires only constant draw calls for batch occlusion tests, while avoiding costly iteration for hierarchy traversal. Our experiments prove our solution outperforms the existing solutions in terms of scalability, culling efficiency, and occlusion‐query performance.