One Framework, Diverse Information Deficiencies: Towards Unified 3D Gaussian Reconstruction under Imperfect Observations

Real-world 3D reconstruction is often limited by diverse imperfect observations, ranging from incomplete scene coverage to degraded image quality. Despite their different manifestations, we argue that they share a common underlying problem: insufficient reliable scene information for faithful reconstruction. We unify them under the notion of information deficiency and introduce UniFill3D, a unified framework for 3D Gaussian reconstruction under imperfect observations. UniFill3D couples scene-grounded reconstruction, which preserves scene-specific geometry and multi-view consistency, with external prior recovery, which recovers missing, degraded, or unreliable visual information. Their target-aligned predictions are reconciled through hierarchical scene–prior fusion, combining dual-latent single-step diffusion with multi-scale prior-guided decoding. The resulting restored views are then fed back to refine the 3D Gaussian representation. We validate UniFill3D on four representative settings spanning incomplete and degraded observations: sparse-view reconstruction, 3D super-resolution, 3D deblurring, and 3D low-light enhancement. UniFill3D achieves state-of-the-art performance across all four tasks, with ablations confirming the complementary value of scene grounding and external priors. Together, these results support viewing seemingly disparate 3D reconstruction problems as different manifestations of a shared information-deficiency challenge that can be addressed through a common reconstruction paradigm.