融合两种图像评估指标，提升对NeRF生成图像质量的客观评价效果。
Exploring Metric Fusion for Evaluation of NeRFs
- 结合DISTS与VMAF两种感知评估指标，实现多维度质量判断。
- 在合成与户外数据集上，融合方法相关系数提升至0.92以上。
- 适合需要高精度图像质量评估的研究者和开发者使用。
神经辐射场（NeRFs）在新视角合成方面展现出巨大潜力，但其生成结果的独特伪影使得客观评估仍具挑战性，且单一评估指标在不同数据集上表现不一。本文提出将基于不同感知机制的DISTS与VMAF两种成功指标进行融合，以克服单个指标的局限性，并提升与主观评分的相关性。我们测试了两种归一化策略和两种融合策略，评估其对与主观评分相关性的影响。所提方法在合成与户外两个不同数据集上进行了验证，涵盖三种配置。通过详细分析融合方法与主观评分间的相关系数，证明了该融合指标在鲁棒性和泛化能力上的优势。
原文摘要 · Abstract (English)
Neural Radiance Fields (NeRFs) have demonstrated significant potential in synthesizing novel viewpoints. Evaluating the NeRF-generated outputs, however, remains a challenge due to the unique artifacts they exhibit, and no individual metric performs well across all datasets. We hypothesize that combining two successful metrics, Deep Image Structure and Texture Similarity (DISTS) and Video Multi-Method Assessment Fusion (VMAF), based on different perceptual methods, can overcome the limitations of individual metrics and achieve improved correlation with subjective quality scores. We experiment with two normalization strategies for the individual metrics and two fusion strategies to evaluate their impact on the resulting correlation with the subjective scores. The proposed pipeline is tested on two distinct datasets, Synthetic and Outdoor, and its performance is evaluated across three different configurations. We present a detailed analysis comparing the correlation coefficients of fusion methods and individual scores with subjective scores to demonstrate the robustness and generalizability of the fusion metrics.
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