Comments (1)
Hi @YuraYelisieiev,
Your code saves the model as a dense representation, so it's expected that the size grows roughly cubically with resolution as LOD increases. What you want is the sparse representation without the empty voxels, which can be found in SOL_NGLOD.py
. IIRC, it takes a dense, trained NGLOD and converts it to its sparse octree representation. The save()
function should give you the right memory footprint per level.
You can also look at the more recent SPC codebase, which will train sparsely out-of-the-box.
Hope this helps,
Joey
from nglod.
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from nglod.