首个针对3DGS压缩视频的评估基准，解决真实场景下质量评测难题。
3DGS-VBench: A Comprehensive Video Quality Evaluation Benchmark for 3DGS Compression
- 构建660个压缩3DGS模型，覆盖11个场景与6种主流压缩算法。
- 通过50人主观评分验证数据可靠性，提供可信赖的视觉质量参考。
- 适合作为压缩算法优化与视觉质量评估模型训练的基准工具。
3D高斯点云（3DGS）可实现高保真实时新视角合成，但存储开销大，促使当前先进方法引入压缩模块。然而，这些生成式压缩技术引入了独特失真，缺乏系统性质量评估研究。为此，我们建立3DGS-VBench，一个大规模视频质量评估（VQA）数据集与基准，包含660个压缩3DGS模型及由11个场景中6种SOTA压缩算法生成的视频序列，参数设置系统化。经50名参与者标注并剔除异常值后，获得可靠平均意见分数（MOS）。我们对6种3DGS压缩算法在存储效率与视觉质量上进行基准测试，并评估15种质量评估指标在多种范式下的表现。本工作支持针对3DGS的专用VQA模型训练，推动压缩与质量评估研究发展。数据集已开源：https://github.com/YukeXing/3DGS-VBench。
原文摘要 · Abstract (English)
3D Gaussian Splatting (3DGS) enables real-time novel view synthesis with high visual fidelity, but its substantial storage requirements hinder practical deployment, prompting state-of-the-art (SOTA) 3DGS methods to incorporate compression modules. However, these 3DGS generative compression techniques introduce unique distortions lacking systematic quality assessment research. To this end, we establish 3DGS-VBench, a large-scale Video Quality Assessment (VQA) Dataset and Benchmark with 660 compressed 3DGS models and video sequences generated from 11 scenes across 6 SOTA 3DGS compression algorithms with systematically designed parameter levels. With annotations from 50 participants, we obtained MOS scores with outlier removal and validated dataset reliability. We benchmark 6 3DGS compression algorithms on storage efficiency and visual quality, and evaluate 15 quality assessment metrics across multiple paradigms. Our work enables specialized VQA model training for 3DGS, serving as a catalyst for compression and quality assessment research. The dataset is available at https://github.com/YukeXing/3DGS-VBench.
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