Scaling 3D Gaussian Splatting: Custom Gradients vs Autograd Under the Hood скачать в хорошем качестве

Scaling 3D Gaussian Splatting: Custom Gradients vs Autograd Under the Hood

In this video, we break down the backward pass of 3D Gaussian Splatting (3DGS) and compare custom gradient implementations vs PyTorch Autograd — step by step, under the hood. We walk through the performance trade-offs between writing a custom CUDA backward pass and relying on automatic differentiation. 📬 Join the 3D Gaussian Splatting & 3D Vision Newsletter: https://3dgs.teachable.com/p/newslett... 🎓 Full 3D Gaussian Splatting Course: https://3dgaussiansplattingcourse.com/ ▶️ Forward Pass Implementation Explained:    • 3D Gaussian Splatting | 3DGS Implementatio...   You’ll learn: • When Autograd becomes a bottleneck • Memory and performance considerations for large-scale 3DGS training This video is ideal if you are: • Modifying or extending the original 3DGS implementation • Building differentiable rendering research prototypes • Optimizing large-scale 3D scene reconstruction • Integrating 3DGS with NeRF or SfM pipelines • Working on custom CUDA or PyTorch ops While the focus is on 3D Gaussian Splatting, the concepts generalize to: • Differentiable rendering systems • Custom CUDA kernels • PyTorch C++/CUDA extensions • Neural rendering optimization • 3D vision and graphics research If you're serious about pushing 3DGS beyond “plug-and-play,” mastering the backward pass is essential — and this video gives you the conceptual clarity and implementation insight to do exactly that. #3DGaussianSplatting #3DGS #DifferentiableRendering #BackwardPass #Autograd #NeRF #ComputerVision #GraphicsProgramming #3DReconstruction #CUDA #PyTorch