首个针对高斯点云渲染质量的综合评测基准
GS-QA: Comprehensive Quality Assessment Benchmark for Gaussian Splatting View Synthesis
- 构建主观评价实验，测试多种主流静态高斯点云方法
- 18种客观指标在真实人类感知下表现差异显著
- 提供完整视频与评分数据集，可作通用评测基准
高斯点云（Gaussian Splatting, GS）为实时3D场景渲染提供了优于神经辐射场（NeRF）的替代方案，通过3D高斯分布表示复杂几何与外观，实现更快渲染速度和更低内存占用。然而，对GS生成静态内容的质量评估尚未深入探索。本文开展了一项主观质量评估研究，评估多种先进静态GS方法在多样化视觉场景中生成的合成视频质量，涵盖360°与前向视角（FF）相机轨迹。同时分析了18种客观质量指标在主观评分下的表现，揭示其优势、局限性及与人类感知的契合度。所有视频与评分数据均公开，形成一个可用于评估GS视图合成与客观质量指标的综合性基准数据库。
原文摘要 · Abstract (English)
Gaussian Splatting (GS) offers a promising alternative to Neural Radiance Fields (NeRF) for real-time 3D scene rendering. Using a set of 3D Gaussians to represent complex geometry and appearance, GS achieves faster rendering times and reduced memory consumption compared to the neural network approach used in NeRF. However, quality assessment of GS-generated static content is not yet explored in-depth. This paper describes a subjective quality assessment study that aims to evaluate synthesized videos obtained with several static GS state-of-the-art methods. The methods were applied to diverse visual scenes, covering both 360-degree and forward-facing (FF) camera trajectories. Moreover, the performance of 18 objective quality metrics was analyzed using the scores resulting from the subjective study, providing insights into their strengths, limitations, and alignment with human perception. All videos and scores are made available providing a comprehensive database that can be used as benchmark on GS view synthesis and objective quality metrics.
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