DIGS: Diffusion Priors Guided by Information Gain for Extrapolative Gaussian Splatting

Recent advances in neural radiance fields and 3D Gaussian Splatting (3DGS) have enabled photorealistic, real-time novel view synthesis. However, reconstruction quality often degrades severely at extrapolated views that deviate from the training views. Existing methods improve extrapolative reconstruction by generating pseudo ground truths at pseudo views with diffusion models. Yet they overlook either the information gain at pseudo views or the reliability of the generated images, both of which are pivotal to effectively integrating diffusion priors. In this work, we introduce DIGS, a framework that integrates Diffusion priors guided by Information gain for extrapolative Gaussian Splatting. We first propose a Group-Block-Diagonal (GBD) approximation of the Fisher information matrix to accurately and efficiently estimate the information gain of pseudo views. We further propose Reliable Information Gain (RIG), which accounts for the reliability of the diffusion-generated pseudo ground truths. Building on RIG, DIGS selects informative and reliable pseudo views and optimizes the 3DGS model in an uncertainty-aware manner. Experiments on three challenging benchmarks show that DIGS consistently outperforms existing methods, enabling a more principled and effective integration of diffusion priors into 3DGS.