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