MoE-GS: π-Gated Mixture-of-Experts for Content-Adaptive 2D Gaussian Image Representation

Explicit Gaussian image representations support direct spatial rendering, but their quality–storage trade-off depends on the precision of every stored attribute. We investigate the interaction between shared appearance, quantization, and truncated rendering in MoE-GS. Each Gaussian combines a small shared color table through continuous mixture coordinates, while a learned positive amplitude participates in both top-$K$ selection and normalized blending. We formulate the storage break-even condition, characterize the color and selection constraints, and optimize the representation through its quantized inference path. A self-contained bitstream makes the reported quality reproducible from the stored bytes alone. Our evaluation distinguishes 16-bit uniform quantization from IEEE FP16, combines fixed-budget joint fitting with fixed-geometry conditional controls, and measures component interventions without changing geometry. The results delimit when appearance sharing is useful and when its quantization error outweighs its storage savings. Region queries retain the global tile partition; their cost includes candidate discovery rather than only the final rasterization.