提出多不确定性高斯点云质量评估方法与数据集，提升3D重建评估可靠性。
MUGSQA: Novel Multi-Uncertainty-Based Gaussian Splatting Quality Assessment Method, Dataset, and Benchmarks
- 基于多不确定性设计贴近人眼观察的主观评估方法
- 构建包含视图数量、分辨率等不确定性的MUGSQA数据集
- 提供双基准评测不同方法鲁棒性与评估指标性能
高斯点云（Gaussian Splatting, GS）作为新兴的3D物体重建技术，以显著提升的重建速度实现高质量渲染。随着各类变体不断涌现，如何评估基于GS方法重建的3D物体感知质量仍是一个开放挑战。为此，我们首先提出一种统一的多距离主观质量评估方法，更真实地模拟实际应用中人类观看行为，以更好地收集感知体验。基于该方法，我们构建了名为MUGSQA的新一代GS质量评估数据集，其设计考虑了输入数据的多重不确定性，包括输入视图的数量与分辨率、视角距离以及初始点云精度。此外，我们建立了两个基准：一个用于评估不同GS重建方法在多种不确定性下的鲁棒性；另一个用于评估现有质量评估指标的性能。相关数据集与代码已公开于https://github.com/Solivition/MUGSQA。
原文摘要 · Abstract (English)
Gaussian Splatting (GS) has recently emerged as a promising technique for 3D object reconstruction, delivering high-quality rendering results with significantly improved reconstruction speed. As variants continue to appear, assessing the perceptual quality of 3D objects reconstructed with different GS-based methods remains an open challenge. To address this issue, we first propose a unified multi-distance subjective quality assessment method that closely mimics human viewing behavior for objects reconstructed with GS-based methods in actual applications, thereby better collecting perceptual experiences. Based on it, we also construct a novel GS quality assessment dataset named MUGSQA, which is constructed considering multiple uncertainties of the input data. These uncertainties include the quantity and resolution of input views, the view distance, and the accuracy of the initial point cloud. Moreover, we construct two benchmarks: one to evaluate the robustness of various GS-based reconstruction methods under multiple uncertainties, and the other to evaluate the performance of existing quality assessment metrics. Our dataset and code are available at https://github.com/Solivition/MUGSQA.
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