标题： NVS-SQA：探索无参考的神经合成场景的自监督质量表征学习 显示英文标题

标题： NVS-SQA: Exploring Self-Supervised Quality Representation Learning for Neurally Synthesized Scenes without References

摘要： 神经视图合成（NVS），如NeRF和3D高斯点云，能够从稀疏视角有效生成逼真的场景，通常通过PSNR、SSIM和LPIPS等质量评估方法进行评估。 然而，这些全参考方法通过将合成视图与参考视图进行比较，可能无法完全捕捉神经合成场景（NSS）的感知质量，特别是由于密集参考视图的可用性有限。 此外，获取人类感知标签的挑战阻碍了大规模标注数据集的创建，可能导致模型过拟合和泛化能力下降。 为了解决这些问题，我们提出了NVS-SQA，这是一种 NSS 质量评估方法，通过自监督学习来学习无参考质量表示，而无需依赖人工标签。 传统的自监督学习主要依赖于“同一实例，相似表示”的假设和大规模数据集。 然而，鉴于这些条件在 NSS 质量评估中并不适用，我们采用了启发式线索和质量分数作为学习目标，并结合专门的对比对准备过程，以提高学习的有效性和效率。 结果表明，NVS-SQA在17种无参考方法中表现优异（即在SRCC上平均高出109.5%，PLCC上高出98.6%，KRCC上高出91.5%），甚至在所有评估指标上都超过了16种全参考方法（即在SRCC上高出22.9%，PLCC上高出19.1%，KRCC上高出18.6%）。 显示英文摘要

摘要： Neural View Synthesis (NVS), such as NeRF and 3D Gaussian Splatting, effectively creates photorealistic scenes from sparse viewpoints, typically evaluated by quality assessment methods like PSNR, SSIM, and LPIPS. However, these full-reference methods, which compare synthesized views to reference views, may not fully capture the perceptual quality of neurally synthesized scenes (NSS), particularly due to the limited availability of dense reference views. Furthermore, the challenges in acquiring human perceptual labels hinder the creation of extensive labeled datasets, risking model overfitting and reduced generalizability. To address these issues, we propose NVS-SQA, a NSS quality assessment method to learn no-reference quality representations through self-supervision without reliance on human labels. Traditional self-supervised learning predominantly relies on the "same instance, similar representation" assumption and extensive datasets. However, given that these conditions do not apply in NSS quality assessment, we employ heuristic cues and quality scores as learning objectives, along with a specialized contrastive pair preparation process to improve the effectiveness and efficiency of learning. The results show that NVS-SQA outperforms 17 no-reference methods by a large margin (i.e., on average 109.5% in SRCC, 98.6% in PLCC, and 91.5% in KRCC over the second best) and even exceeds 16 full-reference methods across all evaluation metrics (i.e., 22.9% in SRCC, 19.1% in PLCC, and 18.6% in KRCC over the second best).