Published 12/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.02 GB | Duration: 4h 51m
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

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