NeRF Explained: Build a Neural Radiance Field from Scratch

Create breathtaking 3D scenes with just a set of 2D photos using Neural Radiance Fields (NeRF)! In this video, we dive deep into how NeRF works, how to build one, and why it’s transforming computer vision and graphics.

🔍 What you'll discover:
✅ What is NeRF? – Understanding the core concept of volumetric rendering and radiance fields.

✅ How NeRF Works – Ray tracing, volumetric integration, positional encoding, MLP architecture.

✅ NeRF Architectures – Original NeRF, Instant‑NGP, mip‑NeRF, and more.

✅ Hands‑On Build – Step‑by‑step Python/PyTorch NeRF implementation.

✅ Performance Tips – Speed up rendering with multi‑resolution hash grids and GPU optimizations.

✅ Applications & Use Cases – From 3D assets to AR/VR and digital twins.

✅ Future Directions – Dynamic NeRF, NeRF in robotics, real‑time deployment.

By the end, you’ll grasp how to generate high‑fidelity 3D models from images, how to train your own NeRF, and where this cutting‑edge tech is heading! 🌐

🔗 Resources & Links

📄 NeRF paper(s) & key extensions

🧰 GitHub repo: [LinkToRepo]

🎥 Related guide: “Instant‑NGP Explained”

#NeRF #NeuralRadianceFields #3DReconstruction #ComputerVision #Graphics #PythonTutorial #DeepLearning #InstantNGP #mipNeRF #ARVR #DigitalTwins #3DModeling