Efficient sorting free 3d gaussian rendering through hybrid sampling and reconstruction
Gaussian Stippling: Efficient Sorting-Free 3D Gaussian Rendering through Hybrid Sampling and Spatiotemporal Reconstruction
GraphicsComputer Vision and Pattern Recognition
Summary
Rendering 3D images made of overlapping fuzzy spots usually needs careful sorting, which is slow. The authors introduce a way to skip sorting by turning these spots into random dots and then cleaning up the noisy results with a smart network. This method works well even on phones and can produce clear, stable images quickly without extra preparation. If trained for a specific scene, it can also create very high-quality images for demanding uses.
What this means in practice
- •For mobile app developers: Implement interactive 3D Gaussian rendering on mobile devices without slow depth sorting or heavy preprocessing.
- •For game graphics programmers: Create visually stable and efficient rendering of scenes with overlapping Gaussian primitives for real-time applications.
Authors
Zijian Huang, Suiliang Mai, Chuankun Zheng, Yuan Meng, Yuchi Huo
Abstract
Conventional 3D Gaussian Splatting (3DGS) requires depth sorting and ordered alpha blending to correctly render overlapping Gaussian primitives. Stochastic transparency enables sorting-free rendering by replacing fractional alpha contributions with discrete stochastic visibility samples, but produces substantial spatial and temporal noise at low sample counts. We refer to this conversion from continuous Gaussian splats to discrete visibility samples as \textit{Gaussian Stippling}. Based on this, we present an efficient order-independent rendering and reconstruction framework that operates directly on unmodified 3DGS assets. Our method adaptively integrates primitive-based and fragment-based stippling, leveraging their complementary strengths across different rendering regimes to significantly improve rendering throughput. To recover high-quality images from sparse stochastic samples, we further introduce a lightweight Gaussian-aware spatiotemporal reconstruction network. By exploiting the Gaussian attributes retained by each stipple, the network aggregates structured stochastic clues across both space and time, effectively suppressing stippling noise. Experiments show that our hybrid Gaussian stippling method, coupled with a spatiotemporal reconstruction network trained on diverse scenes, generalizes to unseen scenes and enables interactive, temporally stable, and visually plausible rendering on mobile devices without retraining or preprocessing. With scene-specific training and appropriately scaled sampling and network capacity, our method further outperforms the baselines in visual quality, offering a high-fidelity configuration for quality-prioritized applications.