Abstract

Neural rendering has recently emerged as a powerful technique for generating photorealistic 3D content, enabling high levels of visual fidelity. In parallel, web technologies such as WebGL and WebGPU support platform-independent, in-browser rendering, allowing broad accessibility without the need for platform-specific installations. The convergence of these two advancements opens up new possibilities for delivering immersive, high-fidelity 3D experiences directly through the web. However, achieving such integration remains challenging due to strict real-time performance requirements and limited client-side resources. To address this, we propose a hybrid rendering framework that offloads high-fidelity 3D Gaussian Splatting(3DGS)/4D Gaussian Splatting(4DGS) processing to a server, while delegating lightweight mesh rendering and final image composition to the client via depth-based screen-space fusion. This architecture ensures consistent performance across heterogeneous devices, reduces client-side memory usage, and decouples rendering logic from the client, allowing seamless integration of evolving neural models without frequent re-engineering. Empirical evaluations show that the proposed hybrid architecture reduces the computational burden on client devices while consistently maintaining performance across diverse platforms. These results highlight the potential of our framework as a practical solution for accessible, high-quality neural rendering across diverse web platforms. (The project page is available at: \url{https://streamsplat.pengpark.com/})