The article here (http://extremelearning.com.au/an-improved-version-of-bridsons-algorithm-n-for-poisson-disc-sampling/) describes the use of some epsilon value seen here as a work around for floating point precision issues, by adding some small value to the radius when generating new points. I have enabled a lint to highlight locations where we lose precision thanks to clippy::cast_possible_truncation. Maybe by eliminating these we can also eliminate the epsilon?
It should be noted that this epsilon will probably cause the algorithm to perform strangely on small ranges / radii so as a workaround making it a function of the radius is probably a good mitigation.
The article here (http://extremelearning.com.au/an-improved-version-of-bridsons-algorithm-n-for-poisson-disc-sampling/) describes the use of some epsilon value seen here as a work around for floating point precision issues, by adding some small value to the radius when generating new points. I have enabled a lint to highlight locations where we lose precision thanks to
clippy::cast_possible_truncation. Maybe by eliminating these we can also eliminate the epsilon?It should be noted that this epsilon will probably cause the algorithm to perform strangely on small ranges / radii so as a workaround making it a function of the radius is probably a good mitigation.