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https://issues.apache.org/jira/browse/LUCENE-8396?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16543776#comment-16543776
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David Smiley commented on LUCENE-8396:
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I took a peek at the patch. One bit surprised me. There is an optimization
with a comment as follows:
{noformat}
// If all docs have exactly one value and the cost is greater
// than half the leaf size then maybe we can make things faster
// by computing the set of documents that do NOT match the query
{noformat}
But isn't that impossible to detect when the indexed data is comprised of
multiple triangles per document? It uses PointValues.size() to detect this but
that value isn't useful here, right? I'm guessing you copy-pasted this logic
for LatLonPoint code where it does apply.
> Add Points Based Shape Indexing
> -------------------------------
>
> Key: LUCENE-8396
> URL: https://issues.apache.org/jira/browse/LUCENE-8396
> Project: Lucene - Core
> Issue Type: New Feature
> Reporter: Nicholas Knize
> Priority: Major
> Attachments: LUCENE-8396.patch, polyWHole.png, tessellatedPoly.png
>
>
> I've been tinkering with this for a while and would like to solicit some
> feedback. I'd like to introduce a new shape field based on the BKD/Points
> codec to bring much of the Points based performance improvements to the shape
> indexing and search usecase. Much like the existing shape indexing in
> {{spatial-extras}} the shape will be decomposed into smaller parts, but
> instead of decomposing into quad cells (which have the drawback of precision
> accuracy and sheer volume of terms) I'd like to explore decomposing the
> shapes into a triangular mesh; similar to gaming and computer graphics. Not
> only does this approach reduce the number of terms, but it has the added
> benefit of better accuracy (precision is based on the index encoding
> technique instead of the spatial resolution of the quad cell).
> For better clarity, consider the following illustrations (of a polygon in a 1
> degree x 1 degree spatial area). The first is using the quad tree technique
> applied in the existing inverted index. The second is using a triangular mesh
> decomposition as used by popular OpenGL and javascript rendering systems
> (such as those used by mapbox).
> !polyWHole.png!
> Decomposing this shape using a quad tree results in 1,105,889 quad terms at 3
> meter spatial resolution.
> !tessellatedPoly.png!
>
> Decomposing using a triangular mesh results in 8 triangles at the same
> resolution as {{encodeLat/Lon}}.
> The decomposed triangles can then be encoded as a 6 dimensional POINT and
> queries are implemented using the computed relations against these triangles
> (similar to how its done with the inverted index today).
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