DPGS: A Diffusion-Prior Guided Framework for Large-Scale 3D Gaussian Splatting Reconstruction

High-fidelity reconstruction of large-scale aerial scenes using3DGS confronts dual challenges: restricted nadir views result in facade"blind spots" and geometric collapse in textureless regions, while themassive scale of high-resolution data induces severe computational loadimbalance during parallel training. In this paper, we propose DPGS, aholistic framework that orchestrates visual foundation model priors andgenerative diffusion priors to achieve robust reconstruction. First, we in-troduce a foundation model-driven pixel-wise dense initialization to mit-igate geometric incompleteness from sparse SfM points, effectively pre-venting floating artifacts in weak-texture areas. Second, to address com-putational bottlenecks, we propose an observation density balanced par-titioning method. By formulating a graph partitioning problem weightedby feature track lengths and observation frequency, we dynamically bal-ance the rendering load across sub-blocks. Finally, we devise a diffusion-prior guided geometry completion mechanism. Leveraging a 3D-awarediffusion model with spiraling side-view sampling, we recover texturesfor unobserved facade blind spots. DPGS achieves state-of-the-art ren-dering quality, geometric completeness, and efficiency across standardbenchmarks and a self-collected dataset. Project page: https://zsddd.github.io/dpgs.