Hierarchical Raster Occlusion Culling

Abstract

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.