MAC-Splat: Multi-Attribute Consistency for High-Fidelity Sparse-View Reconstruction

Reconstructing high-fidelity 3D scenes from sparse views re-mains a central problem in generalizable neural rendering. Existing gen-eralizable 3D Gaussian Splatting (3DGS) methods often exhibit geomet-ric artifacts in sparse-view settings, since supervision based solely on 2Dphotometric losses cannot resolve depth and correspondence ambigui-ties. To address this issue, we propose MAC-Splat, a training frameworkbuilt around direct 3D consistency supervision. MAC-Splat builds onthe MASt3R geometric backbone and a frozen DINOv3 encoder to ob-tain semantically informed 2D correspondences, which serve as geometricanchors for 3D supervision. Using these anchors, we define the Multi-Attribute Consistency (MAC) loss. This objective jointly regularizes the3D attributes of matched Gaussians, including their position, shape,and appearance, by enforcing agreement in a common world coordinateframe. The formulation is robust to outliers and respects the geometry ofcovariance matrices, which leads to stable training under sparse-view con-ditions. Experiments on ScanNet++ show that MAC-Splat outperformsstrong baselines, with particularly large gains under different overlapregimes. In particular, it improves average PSNR over Splatt3R by morethan 4.5 dB, reduces LPIPS, and maintains performance as the camerapose gap increases. These results indicate that a direct, multi-attribute3D consistency objective, when combined with high-quality correspon-dences, is effective for addressing the ill-posed sparse-view reconstructionproblem.