Ben an I are heading out to SIGGRAPH later this week.  I’m planning on blogging during the day this year instead of doing a nightly debrief. To add to the challenge, I’ll be doing it all from my Nexus One.  This post was done on the phone, so it seems plausible.  In fact, I may not pack my laptop at all, leaving more room for books in my luggage.

Drop us a line if you’d like to meet up, or if you want to suggest something not to be missed.

Or just keep an eye out for us.  I’ll be the guy in the SIGGRAPH t-shirt with no laptop.

I was working on a little job today with a 2D temporally variant scalar field.

You know, B&W footage.

I needed to find the parts of the data that were changing the most and compare them to the overall data and the maximum delta.

What I ended up with, once Ben pointed it out to me, was a simple example of calculus laid out in a couple tools.   The simplest case is just taking the frames I have and interpolating the same number of frames, so there’s no missing samples.  It’s silly, really.

But you can try it with other sampling, so there’s also an example of a Sobel filter, with a 1D kernel perpendicular to the normal 2D one.  Cute really.

If you checked out my interactive smoothing comp, you can see how I used a Sobel filter to make the forward facing laser pointer by looking at the differentiation of the R and G channels over time.  Same idea, just different way of expressing the temporal dimension.

I’m tossing in a Laplacian filter too, just for fun, it’s not useful for the calculus part, but it was easy to do, and shows how you can change the kernel to make different effects.  It’s possible to also evaluate 2D or 3D kernels this way, too.  The temporal offsets can be combined with spatial offsets so you could make a 3D blur filter, or a 3D sharpen.  Or a 3D Unsharp Mask, as I’ve also included.

Trying out some new datasets and new techniques…

EDIT:  Jim asked for some more details, and I already had some images that I intended to post, but forgot about.  So here’s a breakdown of the three layers used to make the above image…

Chameleon rendered layers

The left layer is an environment map lookup, the middle is a front lit with high opacity, and the right is a backlit with low opacity.   These were then additively composited together.

I also did some tests on this dataset with clipping.

Chameleon culled with spherical gizmo

Chameleon culled with box

The box culling was an accident, but I thought it looked like a cut of meat that had been chewed on by mice.

Just a little ditty put together for some testing.  Investigating the how the specular highlights look on a low resolution dynamic dataset.