"Chambers" <ben### [at] pacificwebguycom> wrote:
> Just a quick note: a few quick calculations will show that 1600 samples
> is not enough for things like a lightbulb across the room, or the sun in
> the sky, to be effectively used for radiosity.

I feared there would be some catch :}

> > implement some adaptive algorithm. I also think it would be a good
> idea
> > to flag
> > objects as "radiosity targets", like it is done with photons, to
> inform
> > the
> > sampling algorithm about small but bright objects so it can shoot a
> few
> > more
> > rays in that direction.
>
> That would be a great way to deal with what would otherwise be extremely
> large sample sets.

That's why I'm thinking about it :)

Another idea I had was to do such flagging automatically. Say we do the first
samples with a very high sample count. We use some techniques borrowed from
adaptive sampling (though we're not actually going adaptive yet), and if some
close sample rays detect a high gradient (either in color or distance) the area
is somehow marked as "hot".

An area getting less than a certain threshold of "hot" markers will get less
attention during subsequent passes, while areas getting particularly high
numbers of "hot" markers will get increased attention.

However, I'm not sure yet about how to store this type of information,
especially since the perception of what area is "hot" will gradually change
from point to point. Maybe some information attached to each sample, and any
new sample will first check whether there are any samples nearby that know
something about hotspots already.

Just a bunch of wild ideas at the moment.

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