Catch-22

22 days to go, and I'm putting the finishing touches on my raytracer. Above: my sample image, which features implicit surfaces and adaptive anti-aliasing. (Technical details? Bounded Newton-Raphson iterations, gradients for normal vectors. Simple. I'm told regula-falsi is preferred; if I had another day, I'd pop that in there. As for the adaptive anti-aliasing, I'm applying a Sobel operator to the luminance values from the first pass and randomly supersampling pixels above a certain threshold.) Unfortunately, the images seem to suffer from a good deal of noise; in raytracing, this is a sign of numerical instability. If I was pursuing a raytracer-based final project, I would investigate further and fix it, probably along these lines of attack: 

• The problem worsens with distance from the camera. This might be fixed by applying a projective transformation, but that would FUBAR angles for shadows and reflection.
  
• Some of the mesh models have wonky normals; it might be worth the time to recalculate them to the outside.
• I could probably eliminate a few spurious divisions and normalizations.

That said, I don't have time, so noisy images it is. Let's hope my final project is free of such trite errors!