In this thesis we present a conservative, from-region, occlusion culling method based on factorizing the 4D visibility problem into horizontal and vertical components. The visibility of the two components is solved asymmetrically. The horizontal component is based on parameterization of the ray space using two concentric squares. The visibility of the vertical component is solved by incrementally merging umbrae within vertical slices. Based on the horizontal parameterization we decompose the difficult from-region visibility problem in 3D into many simpler from-region problems in 2D vertical slices. The technique is designed so that the horizontal and vertical operations can be efficiently realized together and accelerated by modern graphics hardware. Similar to imagebased from-point methods, we use an occlusion map to encode visibility; however, the image-space occlusion map is in the ray space rather than in the primal space. This map maintains the conservative aggregated umbra created by the occluders processed so far. Our results show that the culling time and the size of the computed potentially visible set depend on the size of the viewcell. For moderate viewcells for walk-through applications, conservative occlusion culling of large urban scenes takes less than a second, and the size of the potentially visible set is only about two times larger than the size of the exact visible set. This work was published in ACM Transactions on Graphics[LSCO03].
The region visibility culling method computes visibility for a region rather than a single point REF .
RAY SPACE FACTORIZATION FOR FROM-REGION VISIBILITY