Abstract

We propose Splatter Layout, a single-view feedforward 3D reconstruction method that jointly predicts Gaussian Splats, mesh, and point cloud in unfolded surface layout aligned with the input image. Unlike prior approaches, which suffer from inconsistent surface representations, our unfolded layout extends reconstruction to invisible regions by predicting parameters from visible neighbors and placing them near adjacent counterparts. To achieve this, we supervise the pipeline using data generated from an unfolding network, ensuring bijective mappings and input-view alignment. Since the pipeline is layout-agnostic, it can be readily extended to diverse object categories, including humans and vehicles. Without modifying the underlying architecture, Splatter Layout substantially improves the geometric fidelity of Splatter Images. Its unfolded layout yields a coherent and interpretable feature organization, resolving prior inconsistencies while establishing dense correspondences with a template mesh for tasks such as animation and appearance editing.