0
$\begingroup$

I am learning about "Image Denoising using Autoencoders". So, now I want to build and train a model. Hence, when I read into how Nvidia generated the dataset, I came across: We used about 1000 different scenes and created a series of 16 progressive images for each scene. To train the denoiser, images were rendered from the scene data at 1 sample per pixel, then 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768, 65536, and 131072 samples per pixel.

I was trying to understand-

1) what is meant by rendering images at n samples per pixel?

2) How to do this in python to generate the dataset?

I have read some articles regarding this but could not form a confident opinion from a Data Science perspective.

https://area.autodesk.com/tutorials/what-is-sampling/

Any leads would be much appreciated! Thanks

$\endgroup$
0
$\begingroup$

Your link is to paid course :) In ray-tracing too few samples will generate something like at the top enter image description here In fact the link with the picture answers your question https://chunky.llbit.se/path_tracing.html

2) Ray-tracing is hard... but not impossible, google for "python ray tracing module"... But something looking close - easily https://stackoverflow.com/questions/22937589/how-to-add-noise-gaussian-salt-and-pepper-etc-to-image-in-python-with-opencv Although actually on the ray-traced images the noise can change because of slope and environment.

If you still want ray-traced noisy images, better to find tutorials for 3D modelling programs, like "ray tracing in 3D Studio MAX tutorial"

| improve this answer | |
$\endgroup$
  • 1
    $\begingroup$ Thanks! That was helpful $\endgroup$ – uttejh Mar 2 at 21:15

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.