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I have a large model (roughly 400k faces) and a large number of points on this model (roughly 2000) and I need to calculate a normal per point based on the closest face on the model. Is there a faster way that isn't O(n*m) where n is the number of points and m is the number of faces (e.g., raycasting or iterating through every face)?

I'm using THREE.js and the target hardware is a Toshiba tablet with Intel integrated graphics so I don't have a lot of power to work with. Currently if I compute this on load it takes about 10+ minutes.

I've been given the requirement that there shouldn't be any model specific information precomputed for each point so that we can use the points on a different model without recomputing everything and then have multiple datasets of point => normal mappings. This is currently what we are doing and it works but doesn't scale well to our needs.

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  • \$\begingroup\$ What do you mean with "closest face on the model"? The closest face from every point (not connected to the point itself)? Or the closest face from a certain perspective? \$\endgroup\$
    – PSquall
    Jun 13, 2018 at 10:41

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Maybe you can set a number of raycasts from each point to different directions. Each raycast will return the closest face. You can than iterate through those. I know it is not a 100% satisfying solution, but maybe it will be good enough for your need?

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