对比多种图像相似性度量，发现新方法DreamSim更适合作为新视角合成的评估工具。
Benchmarking Image Similarity Metrics for Novel View Synthesis Applications
- 用人工损坏图像测试不同相似度指标的敏感性与区分力
- 传统指标对细微像素变化不敏感，而DreamSim能有效识别高质量图像
- 适合评估现实场景中存在轻微渲染瑕疵的新视角合成结果
传统图像相似性度量在评估真实场景图像与人工生成视图之间的相似性时表现不佳。本研究评估了基于感知的新度量方法DreamSim [2]，以及三种常用指标：结构相似性（SSIM）、峰值信噪比（PSNR）和学习型感知图像块相似性（LPIPS）[18, 19）在新视角合成（NVS）中的有效性。我们构建了一个包含人工损坏图像的数据集，以量化各度量指标的敏感性和判别能力。结果表明，传统指标难以区分微小像素变化与严重失真，而DreamSim对微小缺陷更具鲁棒性，能够有效评估图像的高层语义相似性。此外，实验显示DreamSim在评估真实应用场景下的渲染质量方面更为有效，尤其适用于存在轻微渲染瑕疵但不影响人类任务使用的场景。
原文摘要 · Abstract (English)
Traditional image similarity metrics are ineffective at evaluating the similarity between a real image of a scene and an artificially generated version of that viewpoint [6, 9, 13, 14]. Our research evaluates the effectiveness of a new, perceptual-based similarity metric, DreamSim [2], and three popular image similarity metrics: Structural Similarity (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Learned Perceptual Image Patch Similarity (LPIPS) [18, 19] in novel view synthesis (NVS) applications. We create a corpus of artificially corrupted images to quantify the sensitivity and discriminative power of each of the image similarity metrics. These tests reveal that traditional metrics are unable to effectively differentiate between images with minor pixel-level changes and those with substantial corruption, whereas DreamSim is more robust to minor defects and can effectively evaluate the high-level similarity of the image. Additionally, our results demonstrate that DreamSim provides a more effective and useful evaluation of render quality, especially for evaluating NVS renders in real-world use cases where slight rendering corruptions are common, but do not affect image utility for human tasks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。