I am reading the following paper:
I think the idea it presents is summarised by the figures at the top of
the paper. I think the idea is a way of mapping non uniformly spatially
distributed data to a uniformly distributed minimal hash table.
I think it does this by using a hash function of the form: h(p) = h0(p)
+ O[h1(p)] , where h is an index in the hash table, p(x,y,z) are the
spatial coordinates of the data point we wish to hash, and O is an
offset table smaller than the hash table itself.
But that’s as far as my “understanding” of the paper goes.
They keep saying how simple their method is but I don’t find their
explanation of how to actually implement this simple at all. What are
the functions for h0 and h1? How do we calculate the size of h and O and
the offset values for O?
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From a quick scan of the document, it looks to me like h0, h1 & O are
explained in “3.1 Overview & Terminology”. The Value of the Offset Table
“O” is explained in “4.3 Creation of Offset Table”