GaPMeS: Gaussian Patch-Level Mixture-of-Experts Splatting for Computation-Limited Sparse-View Feed-Forward 3D Reconstruction

To address the issues of parameter coupling and high computational demands in existing feed-forward Gaussian splatting methods, we propose Gaussian Patch-level Mixture-of-Experts Splatting (GaPMeS), a lightweight feed-forward 3D Gaussian reconstruction model based on a mixture-of-experts (MoE) multi-task decoupling framework. GaPMeS employs a dual-routing gating mechanism to replace heavy refinement networks, enabling task-adaptive feature selection at the image patch level and alleviating the gradient conflicts commonly observed in shared-backbone architectures. By decoupling Gaussian parameter prediction into four independent sub-tasks and incorporating a hybrid soft–hard expert selection strategy, the model maintains high efficiency with only 14.6 M parameters while achieving competitive performance across multiple datasets—including a Structural Similarity Index (SSIM) of 0.709 on RealEstate10K, a Peak Signal-to-Noise Ratio (PSNR) of 19.57 on DL3DV, and a 26.0% SSIM improvement on real industrial scenes. These results demonstrate the model’s superior efficiency and reconstruction quality, offering a new and effective solution for high-quality sparse-view 3D reconstruction.