Foundations of 3D Machine Learning with PyTorch3D

The world around us is inherently three-dimensional, yet working with 3D data in machine learning can seem daunting. This course demystifies the complexities of 3D machine learning, equipping you with the foundational knowledge and practical skills needed to analyze and build with spatial data. By the end of this course, you will have a clear understanding of 3D data representations, key machine learning techniques for 3D, and the ability to leverage PyTorch3D to develop your own 3D applications and research. What you'll learn: Understand fundamental 3D data representations, including point clouds, meshes, and volumetric data. Learn core deep learning concepts specifically tailored for processing and analyzing 3D information. Apply PyTorch3D for efficient manipulation, transformation, and rendering of 3D data. Explore modern 3D machine learning architectures and techniques, such as Neural Radiance Fields (NeRFs). Practice setting up robust Python development environments for 3D ML projects, managing dependencies effectively. Develop foundational PyTorch3D models for common tasks like 3D object classification and reconstruction. This course begins with the essential mathematical and conceptual building blocks of 3D data, then transitions into an in-depth exploration of PyTorch3D's capabilities. You will progress through practical examples and exercises to solidify your understanding of applying deep learning to 3D problems. This course is designed for absolute beginners to 3D machine learning, requiring no prior experience with 3D modeling or advanced machine learning concepts. Start your journey into the exciting field of 3D machine learning today.