Abstract
We propose an efficient out-of-core octree generation method
for arbitrarily large point clouds. It utilizes a
hierarchical counting sort to quickly split the point cloud
into small chunks, which are then processed in parallel.
Levels of detail are generated by subsampling the full data
set bottom up using one of multiple exchangeable sampling
strategies. We introduce a fast hierarchical approximate
blue-noise strategy and compare it to a uniform random
sampling strategy. The throughput, including out-of-core
access to disk, generating the octree, and writing the final
result to disk, is about an order of magnitude faster than
the state of the art, and reaches up to around 6 million
points per second for the blue-noise approach and up to
around 9 million points per second for the uniform random
approach on modern SSDs.
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