People often mistakenly assume that adaptive sampling means faster rendering. That’s not (technically) true. What adaptive sampling means is even noise convergence. So typical example is:
- You have an image to render that has a few strong highlights in defocused DoF or motion blur
- The image is pretty clean already after 10 minutes/500 passes but takes another 30 minutes/2000 passes for the noise in the defocused highlights to resolve to acceptable level, making rendertime 40 minutes total.
- You enable adaptive sampling. Now your image is pretty clean after 20 minutes instead of 10, but the noise on the highlights is on the same acceptable level as noise everywhere else. So effectively you have cut down rendertime of the noisiest part of the image, but the overall noise level is a bit higher.
What this ultimately means, is that these interior examples you guys are posting won’t benefit from the adaptive sampling that much. There may be some gain in evaluating adaptivity in the more sensorimetric manner (more similar to how human eye sees the image, so in this case a bit more sampling in darker areas), but even that may not bring any observable difference.
So it’s very important to not think of adaptive sampling as universal speed up, but as a tool to make image render evenly, so that you don’t wait hours for that last noisy spot to resolve while rest of the image is excessively clean already.
EDIT: 2 posts below this one you can see a perfect example of this. The noisiest part of the image - the agents face - has the same noise level on both images, yet the adaptive sampled one renders twice as fast, but at the same time, you can see the noise level elsewhere, like on the chair and on the floor are actually worse. Adaptive sampling just made the noise level even across the image.