DeepCR: Deep Cross-Reference Reasoning for Assessing 3D Gaussian Splatting Renderings

Reliable quality assessment is crucial for identifying viewpoint-dependent artifacts and improving 3D Gaussian Splatting (3DGS) reconstruction, yet aligned ground truth is generally unavailable at inference. While no-reference methods lack scene-specific evidence, existing cross-reference methods insufficiently exploit the geometric relevance and complementary semantics of non-aligned observations. In this paper, we propose DeepCR, a novel cross-reference method for dense quality assessment of 3DGS renderings. In particular, pose-aware reference selection reduces viewpoint disparity and filters ambiguous evidence, while multi-level cross-view reasoning over DINOv2 features integrates local distortion cues with viewpoint-robust semantics. A pixel decoder with auxiliary supervision further recovers fine-grained quality variations from coarse patch representations, yielding a full-resolution structural similarity map and an image-level score. Experiments show that DeepCR achieves strong agreement with ground-truth SSIM scores and outperforms the strongest competing CR-IQA method by 8.5 and 11.7 percentage points in average absolute PLCC and SRCC, respectively. The gains in 3DGS reconstruction achieved through DeepCR-guided active view selection further demonstrate the practical value of its quality predictions.