DualDiff3D: Dual Structure-Appearance Diffusion Priors for Reliability-Enhanced 3D Gaussian Splatting

While 3D Gaussian Splatting (3DGS) has revolutionized 3Dreconstruction and novel-view synthesis, scenarios with limited inputviews often lead to poor reconstruction quality and artifacts in ren-dered novel views. Recent efforts attempt to utilize powerful diffusionpriors, yet they typically process rendered and reference views concate-nated along an additional dimension in a single network. These methodsoverlook an inherent nature that different views should maintain ap-pearance similarity but differ in structure due to view shifts, leadingto blur caused by conflicts between the two properties. In this paper,we propose DualDiff, a novel pipeline that leverages dual diffusion pri-ors with a Structure-Appearance Attention (SAA) module to introduce∗ Equal contributionB Corresponding author: zhangjian.sz@pku.edu.cnreference guidance for refining low-quality novel views rendered fromflawed 3D representations. Specifically, we retain one diffusion branchto focus on extracting structural information from the low-quality novelviews, while introducing another branch to ensure appearance consis-tency with reference views. Furthermore, we present a 3D reconstructionframework named DualDiff3D, which integrates a reliability-enhancedRender-Refine-Optimize (RRO) loop to progressively and robustly incor-porate the refined novel views, yielding more accurate 3DGS. Extensiveexperiments demonstrate that our approach outperforms state-of-the-art methods even in the inference-only setting, with further performancegains achievable through training. Our code and pre-trained weights willbe publicly released.