FlowGS: Controlling Information Flow for Graph‐Based Interaction in 3D Gaussian Splatting

Abstract

3D Gaussian Splatting (3DGS) has recently enabled real‐time, high‐quality novel view synthesis. However, its formulation treats each Gaussian independently, lacking explicit information exchange between neighboring elements and limiting its ability to capture local structure. This leads to suboptimal trade‐offs between reconstruction quality and representation compactness. A natural extension is to introduce interactions among neighboring Gaussians, yet naive message passing often results in training instability and over‐smoothing, degrading fine details. In this work, we present FlowGS, a graph‐based interaction framework that explicitly models and controls information flow among Gaussians. Our key insight is that neighborhood interaction in 3DGS is fundamentally a problem of controlling information flow, rather than simply enabling feature propagation. To this end, we represent Gaussians as nodes in a spatial graph to define the flow topology, and enable content‐aware feature aggregation for information exchange. Crucially, we introduce a learnable dynamic gating mechanism that adaptively controls, for each Gaussian, how much information to preserve from itself and how much to incorporate from its neighbors. We further incorporate lightweight regularization and a simple yet effective stabilization strategy to ensure robust and stable optimization. Extensive experiments show that, under the same training budget as vanilla 3DGS, our method consistently achieves higher reconstruction quality while using significantly fewer Gaussians, with up to 6.74 dB PSNR gain (on the Deep Blending playroom scene) and an average reduction of approximately 64.3% in Gaussian count. These results demonstrate that controlling information flow is key to enabling efficient and high‐quality point‐based scene representations.