Stochastic Ray Tracing for the Reconstruction of 3D Gaussian Splatting
Xu, Peiyu, Zhao, Shuang, Sun, Xin, Mullia, Krishna, Fei, Raymond, Georgiev, Iliyan
Ray-tracing-based 3D Gaussian splatting (3DGS) enjoys the generality of supporting non-pinhole camera models and relightable formulations. However, they are usually lacking in performance, partially due to the need for depth-based sorting of all intersecting Gaussians along the traced rays.In this paper, we introduce a sorting-free differentiable stochastic formulation for ray-traced 3DGS, enabling efficient reconstruction and rendering of both standard and relightable 3DGS scenes.For standard 3DGS, our method offers performance comparable to rasterization-based 3DGS and outperforms sorting-based ray tracing.For relightable 3DGS, our technique provides higher-quality reconstructions and renderings thanks to the accurate shadow and shading computation provided by fully ray-traced shadow and light rays.
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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表