# Feed-forward Gaussian Splatting

MoonGS is a feed-forward 3D Gaussian Splatting framework for lunar scenes: given only two images it predicts pixel-aligned Gaussian primitives in a single forward pass with no per-scene optimization, using robust depth features from a frozen vision foundation model (DUSt3R, or VGGT), semantic priors plus a semantic ranking loss to regularize weakly textured background depth, and an entropy-guided heuristic resampling strategy to augment sparse observations. On the LuSNAR benchmark and the authors' weak-texture MoonBlender dataset it outperforms feed-forward NeRF/3DGS baselines on PSNR, SSIM and LPIPS while keeping sub-second inference.

MoonGS is a feed-forward 3D Gaussian Splatting framework for lunar scenes: given only two images it predicts pixel-aligned Gaussian primitives in a single forward pass with no per-scene optimization, using robust depth features from a frozen vision foundation model (DUSt3R, or VGGT), semantic priors plus a semantic ranking loss to regularize weakly textured background depth, and an entropy-guided heuristic resampling strategy to augment sparse observations. On the LuSNAR benchmark and the authors' weak-texture MoonBlender dataset it outperforms feed-forward NeRF/3DGS baselines on PSNR, SSIM and LPIPS while keeping sub-second inference.