# MUGSQA: Novel Multi-Uncertainty-Based Gaussian Splatting Quality Assessment Method, Dataset, and Benchmarks

arXiv: https://arxiv.org/abs/2511.06830 · HTML: https://mlsanity.com/paper/2511.06830/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 32 references that are in the map.
- First posted on arXiv 10 Nov 2025.

## Builds on (most cited first, 25 of 25)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering](https://mlsanity.com/paper/2312.00109/) (2023)
- [Mip-Splatting: Alias-free 3D Gaussian Splatting](https://mlsanity.com/paper/2311.16493/) (2023)
- [Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels](https://mlsanity.com/paper/2312.17090/) (2023)
- [LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS](https://mlsanity.com/paper/2311.17245/) (2023)
- [KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment](https://mlsanity.com/paper/1910.06180/) (2019)
- [DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction](https://mlsanity.com/paper/1905.10711/) (2019)
- [Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network](https://mlsanity.com/paper/1907.02665/) (2019)
- [Octree-GS: Towards Consistent Real-time Rendering with LOD-Structured 3D Gaussians](https://mlsanity.com/paper/2403.17898/) (2024)
- [From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality](https://mlsanity.com/paper/1912.10088/) (2019)
- [EAGLES: Efficient Accelerated 3D Gaussians with Lightweight EncodingS](https://mlsanity.com/paper/2312.04564/) (2023)
- [GaussianObject: High-Quality 3D Object Reconstruction from Four Views with Gaussian Splatting](https://mlsanity.com/paper/2402.10259/) (2024)
- [Point Cloud Quality Assessment: Dataset Construction and Learning-based No-Reference Metric](https://mlsanity.com/paper/2012.11895/) (2020)
- [EmbodiedGen: Towards a Generative 3D World Engine for Embodied Intelligence](https://mlsanity.com/paper/2506.10600/) (2025)
- [Textured Mesh Quality Assessment: Large-Scale Dataset and Deep Learning-based Quality Metric](https://mlsanity.com/paper/2202.02397/) (2022)
- [Just Noticeable Difference for Deep Machine Vision](https://mlsanity.com/paper/2102.08168/) (2021)
- [R3D2: Realistic 3D Asset Insertion via Diffusion for Autonomous Driving Simulation](https://mlsanity.com/paper/2506.07826/) (2025)
- [A Benchmark for Gaussian Splatting Compression and Quality Assessment Study](https://mlsanity.com/paper/2407.14197/) (2024)
- [SJTU-TMQA: A quality assessment database for static mesh with texture map](https://mlsanity.com/paper/2309.15675/) (2023)
- [Sources of Uncertainty in 3D Scene Reconstruction](https://mlsanity.com/paper/2409.06407/) (2024)
- [Evaluating Human Perception of Novel View Synthesis: Subjective Quality Assessment of Gaussian Splatting and NeRF in Dynamic Scenes](https://mlsanity.com/paper/2501.08072/) (2025)
- [GS-QA: Comprehensive Quality Assessment Benchmark for Gaussian Splatting View Synthesis](https://mlsanity.com/paper/2502.13196/) (2025)
- [3DGS-IEval-15K: A Large-scale Image Quality Evaluation Database for 3D Gaussian-Splatting](https://mlsanity.com/paper/2506.14642/) (2025)
- [Gaussian Object Carver: Object-Compositional Gaussian Splatting with surfaces completion](https://mlsanity.com/paper/2412.02075/) (2024)

## Built on by (most cited first, 4 of 4)

- [Scaling Audio-Visual Quality Assessment Dataset via Crowdsourcing](https://mlsanity.com/paper/2602.22659/) (2026)
- [QD-PCQA: Quality-Aware Domain Adaptation for Point Cloud Quality Assessment](https://mlsanity.com/paper/2603.03726/) (2026)
- [GT-PCQA: Geometry-Texture Decoupled Point Cloud Quality Assessment with MLLM](https://mlsanity.com/paper/2603.14951/) (2026)
- [Aes3D: Aesthetic Assessment in 3D Gaussian Splatting](https://mlsanity.com/paper/2605.05155/) (2026)

## Abstract (arXiv)

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.