<h2 id="heading-key-idea">Key Idea</h2>
<p>NeRF presents a method to represent 3D scenes using a fully connected neural network. Given a set of 2D images of a scene, it learns to output the color and volume density of any 3D point, enabling high-quality view synthesis.</p>
<h2 id="heading-why-it-matters">Why It Matters</h2>
<p>3D scene representation and rendering have traditionally required complex data structures and models. NeRF's neural network-based approach provides a compact, continuous, and differentiable representation, pushing the boundaries of view synthesis.</p>
<h2 id="heading-technical-bite">Technical Bite</h2>
<p>NeRF uses a neural network to model the volumetric scene function, and ray marching to render novel views. It leverages spatial coordinates as input and outputs RGB color and volume density.</p>
<h2 id="heading-impact"><strong>Impact</strong></h2>
<p>Sparked interest in using deep learning for 3D scene representation and has influenced various subsequent works in the field of computer graphics and vision.</p>
<h2 id="heading-paper">Paper</h2>
<p>Authors - Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, Ren Ng</p>
<p>Paper - <a target="_blank" href="https://www.matthewtancik.com/nerf">[Link]</a></p>


## Key Idea

NeRF presents a method to represent 3D scenes using a fully connected neural network. Given a set of 2D images of a scene, it learns to output the color and volume density of any 3D point, enabling high-quality view synthesis.

## Why It Matters

3D scene representation and rendering have traditionally required complex data structures and models. NeRF's neural network-based approach provides a compact, continuous, and differentiable representation, pushing the boundaries of view synthesis.

## Technical Bite

NeRF uses a neural network to model the volumetric scene function, and ray marching to render novel views. It leverages spatial coordinates as input and outputs RGB color and volume density.

## **Impact**

Sparked interest in using deep learning for 3D scene representation and has influenced various subsequent works in the field of computer graphics and vision.

## Paper

Authors - Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, Ren Ng

Paper - [\[Link\]](https://www.matthewtancik.com/nerf)

Paper of the Day

Paper of the Day

[Day 2] NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis

Key Idea

Why It Matters

Technical Bite

Impact

Paper