I stopped doing serious rendering work around 2010. Path tracing, BSDFs, Monte Carlo integration were all second nature. From the start of my undergrad in 1997 right up to building the Adobe Ray Tracer in Photoshop in 2010 I had been steeped in this world. Then life moved on: building consumer products at scale, building teams, building platforms. My renderer sat dormant.
Recently I picked it back up both to have something concrete to work on with agents but also to scratch that graphics itch that never went away. Normally to catch up I’d plow through the fifteen years of SIGGRAPH papers that been stacked up, but I did something different.
I started implementing instead.
Read less, build more
There’s a difference between understanding a technique and understanding why it works the way it does. Papers give you the former. Code gives you the latter. I am also a ‘doing’ learner, so for me working on something is how I learn. I’d read many papers on MIS but it wasn’t until actually going and implementing it, working through all the bugs and watching the variance drop that it really locks on.
The problem used to be velocity. Getting from “I understand this algorithm” to “a working implementation” took days to weeks. Boilerplate, scaffolding, debugging the trivial stuff. The interesting parts were buried under setup cost.
Coding agents collapsed that ratio.
The agent workflow, honestly
My workflow isn’t careful line-by-line review. That’s not the point; the speed is the feature. When you can go from a paper to a running implementation of GGX microfacet with VNDF sampling in a fraction of the time it used to take, you get to spend your cognitive budget on the parts that actually require thinking.
What I’ve found is that agents aren’t uniformly fast. Some things, like well-specified algorithms with solid reference implementations(Dupuy-Benyoub spherical cap VNDF sampler for example) they handle cleanly. Others require real steering. Getting light subpath guiding right in BDPT came down to a subtle decision about separate vs. shared guiding fields that no prompt was going to resolve on its own. When separate eye and light fields produce destructive interference at the same surface position, you need to understand why, not just what to type.
That pattern (full speed on clear specs, hard stops where physical insight is required) has been one of the more interesting meta-lessons. More on where the boundary actually falls in a later post as I let that stew more.
The biggest surprise: the field went physical
When I left, biased techniques were the pragmatic answer to hard light transport problems. Dipole approximation for subsurface scattering. Photon mapping as a caustics crutch. Spectral rendering was a research luxury, RGB was good enough.
Coming back, I expected things to have fully moved to the GPU but that trade-off to still be alive.
It isn’t. The field has largely moved to unbiased physical simulation across the board. Random walk SSS has replaced diffusion approximations as the standard. Hero wavelength spectral sampling means that spectral rendering is the default. Null-scattering volume formulations handles participating media properly while being physically based. The question isn’t “can we afford to be physically correct?” anymore.
This landed differently for me than it might for others. My original skin rendering work was dual-purpose: graphics and biomedical light transport simulation. The biomedical side required physical random walks and spectral interaction simulation; you can’t use a dipole approximation when you need to know where photons actually go in tissue. At the time, that work lived in a completely separate world from production rendering. The techniques were too expensive, too specialized.
Now seeing random-walk SSS become the graphics standard felt like watching a conversation finally arrive somewhere you’d been standing for a while.
What’s been implemented so far
In a few weeks, working alongside agents, RISE (the renderer I am modernizing) has gone from a reasonable 2010-era foundation to something a lot closer to where the field is now with things like:
- GGX microfacet with anisotropic VNDF sampling (Dupuy-Benyoub 2023) and Kulla-Conty multiscattering energy compensation
- Random-walk subsurface scattering replacing dipole/diffusion approximations
- Hero wavelength spectral sampling to get spectral rendering with lower color noise
- Null-scattering volume framework for unbiased heterogeneous participating media
- Light BVH for many-light sampling (4.78x variance reduction on a 100-light scene)
- Light subpath guiding in BDPT using separate OpenPGL fields for eye and light paths
- Blue-noise error distribution via ZSobol sampling
Next up: VCM, Hyperspectral skin rendering
Aravind 