Udemy – 3D Gaussian Splatting from Scratch — PyTorch-Only

Unlock the full potential of 3D Gaussian Splatting (3DGS) by building the entire pipeline from scratch using Python and PyTorch — no CUDA, no libraries, just clear, modular, and research-friendly code.

In this course, you’ll dive deep into the cutting-edge neural rendering technique of 3D Gaussian Splatting, gaining hands-on experience with every component:

• Parse COLMAP outputs for camera poses and sparse reconstructions
• Understand and implement 3D Gaussian primitives as scene representations
• Build a trainable neural rendering pipeline that models view-dependent radiance
• Code the training loop and optimization strategy for Gaussian splatting
• Create a real-time differentiable renderer that produces photorealistic images
• Explore visualization techniques for complex 3D data

This course is perfect for researchers, graduate students, and developers who want to:

• Understand every detail of the 3D Gaussian Splatting algorithm
• Gain a clean, flexible PyTorch implementation that can be easily extended for experiments
• Avoid the complexity of C++/CUDA while still achieving state-of-the-art results
• Build a strong foundation in neural rendering and 3D computer vision from a practical coding perspective

What you’ll need to succeed:

• Python programming skills
• Familiarity with PyTorch and NumPy

By the end of the course, you’ll have a full working implementation of 3D Gaussian Splatting that you can modify, extend, and use as a foundation for your own research or projects.