PR-IQA：面向扩散式新视角合成的部分参考图像质量评估
PR-IQA: Partial-Reference Image Quality Assessment for Diffusion-Based Novel View Synthesis
提出部分参考图像质量评估（PR-IQA），用不同位姿的参考图在重叠区域计算几何一致的质量图，再通过交叉注意力补全为全图质量图；可直接用于筛选扩散生成伪真值视图中的高置信区域，为扩散增强的3DGS等重建流程提供免真值的监督掩码。
原文依据 PR-IQA first computes a geometrically consistent partial quality map in overlapping regions.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论扩散模型在稀疏视角新视角合成（NVS）中具有前景，因为它们能够生成伪真值视图，以辅助3D高斯泼溅（3DGS）等三维重建流程。然而，这些合成图像往往包含光度与几何不一致，直接将其用于监督会损害重建质量。为解决这一问题，我们提出部分参考图像质量评估（PR-IQA），该框架利用来自不同位姿的参考图像评估扩散生成的视图，无需真值。PR-IQA首先在重叠区域计算几何一致的部分质量图，随后执行质量补全，将该部分质量图修复为稠密的全图质量图。该补全通过引入参考视图上下文的交叉注意力机制实现，从而保证跨视图一致性并支持全面的质量评估。当集成到扩散增强的3DGS流程中时，PR-IQA将监督限制在其质量图所识别的高置信区域。实验表明，PR-IQA优于现有图像质量评估方法，在无真值监督的情况下达到全参考级别的精度。因此，我们的质量感知3DGS方法能够更有效地过滤不一致，产生更优的三维重建与新视角合成结果。
英文原文摘要 1352 字符
Diffusion models are promising for sparse-view novel view synthesis (NVS), as they can generate pseudo-ground-truth views to aid 3D reconstruction pipelines like 3D Gaussian Splatting (3DGS). However, these synthesized images often contain photometric and geometric inconsistencies, and their direct use for supervision can impair reconstruction. To address this, we propose Partial-Reference Image Quality Assessment (PR-IQA), a framework that evaluates diffusion-generated views using reference images from different poses, eliminating the need for ground truth. PR-IQA first computes a geometrically consistent partial quality map in overlapping regions. It then performs quality completion to inpaint this partial map into a dense, full-image map. This completion is achieved via a cross-attention mechanism that incorporates reference-view context, ensuring cross-view consistency and enabling thorough quality assessment. When integrated into a diffusion-augmented 3DGS pipeline, PR-IQA restricts supervision to high-confidence regions identified by its quality maps. Experiments demonstrate that PR-IQA outperforms existing IQA methods, achieving full-reference-level accuracy without ground-truth supervision. Thus, our quality-aware 3DGS approach more effectively filters inconsistencies, producing superior 3D reconstructions and NVS results.