SurfSplat: Conquering Feedforward 2D Gaussian Splatting with Surface Continuity Priors

TL;DR

3D scene reconstruction from sparse views remains challenging due to geometry-texture disentanglement. SurfSplat proposes a feedforward framework using 2D Gaussian Splatting (2DGS) with a surface continuity prior and forced alpha blending to recover coherent surfaces and faithful textures. It introduces HRRC, a high-resolution rendering-based metric, to reveal subpixel geometric artifacts. Across RealEstate10K, DL3DV, and ScanNet, SurfSplat achieves state-of-the-art results on both standard and HRRC evaluations, demonstrating robust high-fidelity reconstruction from sparse inputs.

Abstract

Reconstructing 3D scenes from sparse images remains a challenging task due to the difficulty of recovering accurate geometry and texture without optimization. Recent approaches leverage generalizable models to generate 3D scenes using 3D Gaussian Splatting (3DGS) primitive. However, they often fail to produce continuous surfaces and instead yield discrete, color-biased point clouds that appear plausible at normal resolution but reveal severe artifacts under close-up views. To address this issue, we present SurfSplat, a feedforward framework based on 2D Gaussian Splatting (2DGS) primitive, which provides stronger anisotropy and higher geometric precision. By incorporating a surface continuity prior and a forced alpha blending strategy, SurfSplat reconstructs coherent geometry together with faithful textures. Furthermore, we introduce High-Resolution Rendering Consistency (HRRC), a new evaluation metric designed to evaluate high-resolution reconstruction quality. Extensive experiments on RealEstate10K, DL3DV, and ScanNet demonstrate that SurfSplat consistently outperforms prior methods on both standard metrics and HRRC, establishing a robust solution for high-fidelity 3D reconstruction from sparse inputs. Project page: https://hebing-sjtu.github.io/SurfSplat-website/

Figures (9)

• Figure 1: SurfSplat is a feedforward network that predicts a 3D scene representation from sparse images input. Previous methods often produce sparse, color-biased pointclouds that lack surface continuity, especially under close-up views. In contrast, our SurfSplat approach utilizes 2DGS with a surface continuity prior and forced alpha blending to generate coherent and realistic 3D surfaces.
• Figure 2: Illustration for model architecture. Given sparse input images, our dual-path encoder processes them through both single-view and multi-view branches. The fused features are passed through a U-Net to predict intermediate attributes, including depth, scale multipliers, and appearance components. Finally, these intermediates are converted into standard Gaussian attributes using our surface continuity prior and forced alpha blending strategy.
• Figure 3: Illustration for Gaussian processor. We visualize how image-space neighboring pixels are transformed into Gaussians aligned on a continuous surface via the surface continuity prior. To prevent opacity collapse and preserve 3D alignment, we apply a forced alpha-blending strategy that reduces opacities, ensuring that spatially occluded Gaussians still contribute during rendering.
• Figure 4: Multi-resolution rendering of 3D scenes. We visualize rendered images and depth maps at three resolutions: $\times1$ (blue box), $\times2$ (green box), and $\times4$ (red box). As resolution increases, artifacts in the underlying 3D representation become more evident. In the image space, they appear as dark regions caused by unfilled gaps, where hollow areas are rendered as black pixels. In the depth space, they appear as unnatural yellow regions, indicating incorrect depth predictions caused by geometric discontinuities or sparsity. Note that yellow corresponds to near surfaces and blue denotes distant regions in depth map visualization.
• Figure 5: Ablation study: Visualization of reconstructed 3D scenes. Our full model yields continuous and coherent surfaces, while ablated variants exhibit visible artifacts and spatial inconsistencies.