Neural Radiance Fields (Nerf)

Introduction to NeRF, volumetric rendering, and 3D reconstruction

What you'll learn

Introduction to reconstruction

Introduction to 3D reconstruction

Introduction to Neural Radiance Fields (NeRF)

Novel view synthesis with NeRF

3D reconstruction with NeRF (mesh extraction)

Introduction to 3D rendering

Requirements

Basic programming knowledge

Basic Machine Learning knowledge

Description

Welcome to this course about Neural Radiance Fields (Nerf)! Neural radiance fields is an innovative technology that is attracting a lot of interest in the world of computer vision. Nerf allows novel view synthesis, and 3D reconstruction, among other things. Since its appearance two years ago, many startups have been created, and as job offers suggest, large technology companies (Meta, Apple, Google, Amazon, …) are using it. In this online course, you will discover: How Nerf models work and how they can be used in various applications How to train and evaluate a Nerf model How to generate novel views from an optimized modelHow to extract a 3D mesh from an optimized modelHow to integrate Nerf into your computer vision projects Examples of real-world use cases for Nerf in the industryOur course is designed for developers and scientists who want to learn about Nerf and use it in their projects. We cover all aspects of setting up and using Nerf, from start to finish. Register now to access our comprehensive online course on Nerf models and learn how this technology can enhance your computer vision projects. Don't miss this opportunity to learn about the latest advances in computer vision with Nerf!

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Introduction to reconstruction - part 1

Lecture 3 Introduction to reconstruction - part 2

Section 2: 3D reconstruction

Lecture 4 Ray tracing and Camera Model

Lecture 5 Camera: visualization

Lecture 6 3D rendering

Lecture 7 Volumetric rendering - part 1

Lecture 8 Volumetric rendering - part 2

Lecture 9 Differentiable rendering & Optimization

Lecture 10 Adding a rotation matrix to the camera: Camera To World

Section 3: 3D reconstruction : modules

Lecture 11 Camera and Dataset - part 1

Lecture 12 Camera and Dataset - part 2

Lecture 13 Volumetric Rendering

Lecture 14 3D model: Voxels

Lecture 15 Machine Learning Optimization loop

Lecture 16 White background regularization

Lecture 17 Mode collapse on synthetic data: solution

Section 4: NeRF : Neural Radiance Fields

Lecture 18 Introduction

Lecture 19 Architecture: implementation

Lecture 20 Positional encoding : implementation

Lecture 21 Results

To engineers and programmers,To entrepreneurs,To students and researchers