Inside the CUDA Kernel: The GPU Implementation of 3D Gaussian Splatting

2026-03-02 · Source: AI Advances - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Advanced, quick

Summary

This bonus part of a series on 3D Gaussian Splatting (3DGS) focuses on the GPU implementation details that enable real-time rendering, specifically addressing how flat arrays, radix sort, and shared memory contribute to achieving over 100 frames per second (FPS). It delves into the algorithmic and conceptual challenges outlined in the original 3D Gaussian Splatting paper by Kerbl et al., rather than providing a CUDA programming tutorial. The content explores how Gaussians are assigned to tiles in parallel, the process of sorting millions of entries concurrently, and strategies for efficient data loading to optimize memory access patterns, referencing specific kernels from the "diff-gaussian-rasterization" library to illustrate these concepts.

Key takeaway

For AI Engineers optimizing real-time 3D rendering pipelines, understanding the GPU implementation of 3D Gaussian Splatting is critical. Focus on how flat arrays, radix sort, and shared memory are employed to achieve high frame rates. Your efforts should prioritize efficient parallel sorting and memory access patterns to maximize performance, especially when scaling to millions of Gaussians.

Key insights

Efficient 3DGS rendering relies on parallel processing, optimized data structures, and memory access patterns.

Principles

• Parallel sorting is crucial for real-time rendering.
• Memory access patterns dictate GPU performance.
• Tiling simplifies parallel Gaussian assignment.

Method

The 3DGS GPU implementation uses flat arrays for data, radix sort for parallel depth sorting, and shared memory for efficient data loading within tiles.

In practice

• Utilize radix sort for large-scale parallel sorting.
• Design data structures for coalesced memory access.
• Implement tiling for concurrent processing.

Topics

• 3D Gaussian Splatting
• GPU Rendering
• CUDA Kernels
• Radix Sort
• Real-time Graphics

Code references

• graphdeco-inria/diff-gaussian-rasterization

Best for: AI Engineer, Deep Learning Engineer, Computer Vision Engineer

Related on AIssential

• The Tricks That Make Production 3DGS Fast (Even If Ours Isn’t) — Optimizations for 3D Gaussian Splatting, like tiling, are critical for real-time GPU rendering but may not benefit CPU implementations.
• Local-GS: Accelerating 3D Gaussian Splatting via Tile-Local Warp Coherence — Local-GS enhances 3DGS rendering by aligning Gaussian processing with GPU warp execution for improved efficiency.
• Splaxel: Efficient Distributed Training of 3D Gaussian Splatting for Large-scale Scene Reconstruction via Pixel-level Communication — Splaxel's pixel-level communication for distributed 3DGS training drastically cuts overhead, enabling efficient scaling for large-scale scene reconstruction.
• AMD-Powered 3D Gaussian Splatting for Autonomous Driving Scenes — Street Gaussians on AMD GPUs enables dynamic 3D scene reconstruction and editing for autonomous driving.
• Less Gaussians, Texture More: 4K Feed-Forward Textured Splatting — LGTM enables 4K novel view synthesis by decoupling geometry from resolution using textured Gaussian primitives.
• Deep Learning Approaches for 3D Medical Scene Completion: From Geometric Modeling to Generative Paradigms — The field of 3D medical scene completion has rapidly evolved from voxel-based methods to generative diffusion and Gaussian splatting.

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI Advances - Medium.