Rethinking Geometric Priors in Novel View Synthesis through Multi-Axis Reliability Fields

Geometric priors are widely used in novel view synthesis (NVS) to incorporate structural guidance. However, recent results show that this conditioning often provides marginal gains under strong backbones. Does this mean that geometric priors are no longer important once the model can already infer cross-view correspondence on its own? Our controlled corruption experiments show otherwise: geometry remains useful, but its value is under-utilized when reliable and unreliable guidance are not distinguished. Motivated by this finding, we introduce Reliability-Calibrated Geometric Attention (RCGA), which models geometric reliability as a multi-axis field to control how explicit geometric evidence modifies the backbone's intrinsic correspondence. The field identifies where and under which model states geometry contributes complementary information. Specifically, the partition-preserving product-of-experts uses the predicted field to calibrate geometric evidence in joint attention for selective correspondence refinement. A comparative oracle supervises the field by measuring the incremental utility of geometry beyond the backbone during training. Across RealEstate10K and ScanNet, RCGA consistently improves epipolar, depth-projection, and coordinate priors over a strong diffusion backbone, achieving state-of-the-art performance while suppressing unreliable geometric guidance.