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Fix typos in the user manual for the dD spatial searching pkg

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Nuno Miguel Nobre 2023-06-14 21:53:16 +01:00
parent 622f7ac884
commit 43d2188068
1 changed files with 4 additions and 4 deletions
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6
Spatial_searching/doc/Spatial_searching/Spatial_searching.txt
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@ -62,7 +62,7 @@ computation has to be re-invoked for a larger number of neighbors,
thereby performing redundant computations. Therefore, Hjaltason and
Samet \cgalCite{hs-rsd-95} introduced <I>incremental nearest neighbor
searching</I> in the sense that having obtained the `k` nearest
neighbors, the `k + 1`st neighbor can be obtained without having
neighbors, the `k + 1`th neighbor can be obtained without having
to calculate the `k + 1` nearest neighbor from scratch.
Spatial searching typically consists of a preprocessing phase and a
@ -426,13 +426,13 @@ how to perform parallel queries:
\section Performance Performance
\subsection OrthogonalPerfomance Performance of the Orthogonal Search
\subsection OrthogonalPerformance Performance of the Orthogonal Search
We took the gargoyle data set (Surface) from aim\@shape, and generated the same number of random points in the bbox of the gargoyle (Random).
We then consider three scenarios as data/queries.
The data set contains 800K points. For each query point we compute the K=10,20,30 closest points, with the default splitter and for the bucket size 10 (default) and 20.
The results were produced with the release 5.1 of \cgal, on an Intel i7 2.3 Ghz
The results were produced with the release 5.1 of \cgal, on an Intel i7 2.3 GHz
laptop with 16 GB RAM, compiled with CLang++ 6 with the O3 option.
The values are the average of ten tests each. We show timings in seconds for both the building of the tree and the queries.