Published 12/2022MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: 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, , ...) 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 eeers and programmers,To entrepreneurs,To students and researchers HomePage:
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