Issue #76 Architecture Edwin Dong, Lalit Julapalli
Neural Radiance Fields (NeRFs), Simply Explained
Free editionPRO edition
This week’s PRO issue is accompanied by a Colab notebook that deep dives into Neural Radiance Fields with an amazing 3D example. As always, the code in this issue can be executed on a normal home PC and does not require a large amount of VRAM.
Imagine taking a picture in the room you’re in right now. Take one step to the right and snap another. The room in this new picture will look different from the first. Take two steps forward and turn around. Now you’ve got a picture that includes objects and features that may not have been in the first two.
But sometimes looking at a picture isn’t enough; we need more than just one angle to determine what an object or scene truly captures. An extra dimension, perhaps.
Neural Radiance Fields (NeRFs) actualize this concept, bringing 2D images into 3D spatial features, which was first introduced by a paper written by Ben Mildenhall and his team in 2020.
NeRF learns scenes that were never photographed by utilizing a continuous function that describes how light (radiance) interacts with and passes through every point in a 3D space.
Instead of relying on triangle meshes like traditional 3D graphics renderers, it sculpts objects by treating the scene as a volume, where each point of space has two key properties: density (how opaque/solid the point is) and color (the RGB values emitted from that point in a given viewing direction).
This edition is for PRO members.
