VG^2GT: Voxel-Gaussian Splatting Visual Geometry Grounded Transformer

Gaussian splatting has shown strong potential for 3D reconstruction and novel view synthesis. However, most existing methods require accurate camera parameters and per-scene optimization, while feed-forward methods with pixel-aligned Gaussian primitives often suffer from artifacts and non-uniform primitives. In this paper, we propose VG^2GT, a Voxel-Gaussian Splatting Visual Geometry-Grounded Transformer. VG^2GT leverages a frozen pretrained visual foundation model (VFM), incorporates a multi-scale differentiable voxel module to enhance geometric understanding, and directly splits and regresses Gaussian primitive parameters from voxel features. During training, depth maps are supervised through stochastic solid volume rendering, enabling geometrically accurate Gaussian scene reconstruction while keeping the visual foundation model fully frozen. This design enables VG^2GT to be seamlessly plugged into any patch-feature-based VFM, while substantially reducing the required training cost. VG^2GT outperforms current state-of-the-art methods on widely used DTU, Replica, TAT, and ScanNet datasets.