ReSplat: Learning Recurrent Gaussian Splatting

While existing feed-forward Gaussian splatting models of-fer computational efficiency and can generalize to sparse view settings,their performance is fundamentally constrained by relying on a singleforward pass for inference. We propose ReSplat, a feed-forward recurrentGaussian splatting model that iteratively refines 3D Gaussians withoutexplicitly computing gradients. Our key insight is that the Gaussiansplatting rendering error serves as a rich feedback signal, guiding therecurrent network to learn effective Gaussian updates. This feedbacksignal naturally adapts to unseen data distributions at test time, enablingrobust generalization across datasets, view counts, and image resolutions.To initialize the recurrent process, we introduce a compact reconstructionmodel that operates in a 16× subsampled space, producing 16× fewerGaussians than previous per-pixel Gaussian models. This substantiallyreduces computational overhead and allows for efficient Gaussian updates.Extensive experiments across varying number of input views (2, 8, 16, 32),resolutions (256×256 to 540×960), and datasets (DL3DV, RealEstate10K,and ACID) demonstrate that our method achieves state-of-the-art perfor-mance while significantly reducing the number of Gaussians and improvingthe rendering speed. Our project page is at haofeixu.github.io/resplat.