SampleSplat: Efficient Feed-Forward 3D Gaussian Splatting via Scene-Aware Spatial Sampling

Feed-forward 3D Gaussian Splatting enables rapid scene reconstruction, but pixel-aligned Gaussian allocation couples representation capacity to input resolution and view count, preventing adaptive allocation of the Gaussian budget according to scene complexity. To address this limitation, we present SampleSplat, a feed-forward framework that decouples Gaussian generation from the input pixel grid through scene-aware probabilistic sampling. Given 3D anchors constructed from image features and estimated geometry, our sampling module jointly learns anchor selection and spatial offsets, enabling Gaussians to be adaptively allocated and positioned under a shared budget. The module is trained end-to-end using only reconstruction supervision, without handcrafted complexity measures or auxiliary allocation labels. As a result, SampleSplat supports variable-view inputs, including monocular input, and flexible input resolutions within a single model, while enabling adaptive allocation under a controllable Gaussian budget Experiments on DL3DV-10K demonstrate state-of-the-art novel view synthesis performance across these settings, with monocular reconstruction taking $90.97$ ms at the native resolution of $540\times 960$.