run.py still contained an early, subtly incorrect PSNR computation that has to do with how prior art in NeRF papers (incorrectly) interprets the PNG alpha channel as sRGB-space rather than linear space. This is further complicated by the fact that NeRF's raymarching procedure also operated in sRGB space (rather than linear), which is both non-physical and introduces a subtle discrepancy with traditional PNG background blending. The new run.py code works around all that -- as the code that generated the paper results already did.

The following line of code will now produce a PSNR of ~36.35, matching the paper and reflecting the actual quality of our results.
`./run.py --mode nerf --scene lego/transforms_train.json --test_transforms lego/transforms_test.json --nerf_compatibility`