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arXiv · 2609.23027

Quality Assessment of 3D Gaussian Splatting: Distortions, Benchmarks, and Open Challenges

Shuai Liu·Binqiang Liu·Qingyu Mao·Jiacong Chen·Yongsheng Liang·Youneng Bao
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Abstract

3D Gaussian Splatting (3DGS) has become a practical scene representation for real-time novel-view rendering, compression, and immersive content delivery. However, its quality assessment still largely follows rendered-view proxy protocols that sample camera poses, render images or videos, and apply inherited image and video quality metrics. While convenient, this practice does not fully capture 3DGS-native degradations that originate from Gaussian primitive distributions, splatting and visibility behavior, and trajectory-dependent artifacts. This survey reviews recent 3DGS quality assessment studies from four perspectives, covering distortion characteristics, subjective benchmarks, objective metric reliability, and emerging directions for native 3DGS evaluation. Across the literature, we find that different benchmarks construct different notions of quality, and that metric effectiveness is highly protocol dependent, varying with distortion sources, stimulus formats, and view and trajectory sampling. Finally, we summarize open challenges and outline practical guidelines for building more comparable and representative 3DGS-QA evaluations.

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Shuai Liu, Binqiang Liu, Qingyu Mao, Jiacong Chen, Yongsheng Liang, Youneng Bao. 2026-09-19. Quality Assessment of 3D Gaussian Splatting: Distortions, Benchmarks, and Open Challenges. https://doi.org/10.1109/icivc70586.2026.11686689

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