GSNorm: An Efficient 3D Gaussian Rendering Accelerator with Splat Normalization and LUT-assist Rasterization
Yiyang Sun, Peiran Yan, Yiqi Jing, Le Ye, Tianyu Jia · 2025
3D Gaussian Splatting recently emerged as the new SOTA approach for many computer graphic tasks. While Gaussian Splatting has demonstrated impressive rendering quality and performance on GPUs, real-time GS rendering on edge devices is still challenging. We identified the unbalanced Rendering pipeline and the uneven Gaussian distribution as the main obstacles to efficient rendering. To address these problems, we present GSNorm, a rendering accelerator with an online quantization preprocessor for per-gaussian coordinate transformation to normalize Gaussian footprints for pixel-wise calculation reduction. A LUT-based quantized rendering design is also presented to break the pipeline data dependency. Furthermore, a depth-guided cluster-sorting unit is incorporated to improve Gaussian sorting efficiency. GSNorm accelerator is implemented and evaluated in TSMC 22 nm technology with several real-world scenes, providing significant rendering efficiency and performance improvements for real-time applications.