Understanding NeRF or Neural Radiance Fields 🧐

It is a method that can synthesize new views of 3D scenes using a small number of input views.

As part of the @weights_biases blogathon (https://t.co/tRddw6jXeA), here are some articles to understand them
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Want to dive head first into some code? 🤿
Here is an implementation of NeRF using JAX & Flax https://t.co/pKO5NDSDqv.

The Report used W&B to track the experiments, compare results, ensure reproducibility, and track utilization of the TPU during the experiment.
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Mip-NeRF 360 is a follow-up work that looks at whether it's possible to effectively represent an unbounded scene, where the camera may point in any direction and content may exist at any distance.

https://t.co/QNY6VuN8zd
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Training a single NeRF does not scale when trying to represent scenes as large as cities.

To overcome this challenge, Block-NeRF was introduced which yields some amazing reconstructions of San Francisco. Here's one of Lombard Street.
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They built their implementation on top of Mip-NeRF, and also combine many NeRFs to reconstruct a coherent large environment from millions of images.

🐝Read more here: