NeRFs: Neural Radiance Fields - Paper Explained

❤️ Support the channel ❤️
   / @aladdinpersson  

Paper: https://arxiv.org/abs/2003.08934
Full title: NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis

Paid Courses I recommend for learning (affiliate links, no extra cost for you):
⭐ Machine Learning Specialization https://bit.ly/3hjTBBt
⭐ Deep Learning Specialization https://bit.ly/3YcUkoI
📘 MLOps Specialization http://bit.ly/3wibaWy
📘 GAN Specialization https://bit.ly/3FmnZDl
📘 NLP Specialization http://bit.ly/3GXoQuP

✨ Free Resources that are great:
NLP: https://web.stanford.edu/class/cs224n/
CV: http://cs231n.stanford.edu/
Deployment: https://fullstackdeeplearning.com/
FastAI: https://www.fast.ai/

💻 My Deep Learning Setup and Recording Setup:
https://www.amazon.com/shop/aladdinpe...

GitHub Repository:
https://github.com/aladdinpersson/Mac...

✅ One-Time Donations:
Paypal: https://bit.ly/3buoRYH

▶️ You Can Connect with me on:
Twitter -   / aladdinpersson  
LinkedIn -   / aladdin-persson-a95384153  
Github - https://github.com/aladdinpersson


Timestamps:
0:00 - Introduction
0:54 - Goal of NeRFs
2:36 - NeRF network architecture
4:35 - The key idea you need to understand
6:17 - How NeRFs learn and work
12:15 - Intuition behind volume rendering
15:15 - Loss function
15:55 - Trick #1: Positional Encoding
17:36 - Trick #2: Hierarchical sampling
19:35 - Ending