Neural Radiance Fields (NeRF) — Turning 2D Images into 3D Scenes3 min read·Just now--Ever heard of it?Convert image into 3DImagine taking a few photos of a statue from different angles and then being able to spin around it in 3D on your computer. Sounds magical, right? That’s exactly what Neural Radiance Fields (NeRF) can do. In this article, we’ll walk you through how NeRF works, why it’s exciting, and how it’s opening new doors in computer vision and AI.Not a member? Read here :- Link🚀 What Is NeRF?NeRF stands for Neural Radiance Fields. It’s a deep learning technique that takes a handful of 2D images of a scene and generates a realistic 3D model. You can then view the scene from any angle — even those not originally captured in the photos.Think of NeRF as giving AI-powered eyes to a machine: it doesn’t just see the images; it understands how light and geometry work together to create a 3D world.🤔 Why Not Just Use 3D Scanners?Good question! Traditional 3D reconstruction methods like LiDAR or photogrammetry require expensive hardware or lots of processing time. NeRF is data-efficient (just a few images needed) and surprisingly accurate, especially when trained well.🔮 How NeRF Works (The Intuitive Way)Let’s simplify the magic:📸 Step 1: Feed It Some PhotosYou start by taking photos from different angles around an object. You also provide the camera positions (a.k.a. poses).🌌 Step 2: A Virtual 3D SpaceInstead of building a traditional 3D mesh, NeRF imagines the world as a continuous volume. Every point in 3D space can have color and density.🔦 Step 3: Shoot Rays into the SceneFor every pixel in the image, NeRF:Shoots a virtual ray from the cameraSamples multiple 3D points along the rayUses a neural network to predict the color and density at each pointBlends these predictions together using a technique called volume rendering