# GaussFlaw: Fine-Grained Perceptual Diagnosis for 3D Gaussian Splatting

**Authors:** Shaocheng Shen, Zihan Zheng, Kejin Li, Lu Liu, Xiaoyun Zhang, Qiang Hu, Wenjun Zhang  
**Lab:** Shanghai Jiao Tong University · **Published:** 2 Nov 2026  
**Links:** [arXiv](https://arxiv.org/abs/2611.02767) · [PDF](https://arxiv.org/pdf/2611.02767) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2611.02767.html)

## Key numbers

- **80.08%**: Experiments show that GeoCritic achieves an AUC-J of 0.9507 and an Acc@1 of 80.08%
- **14,289**: GaussFlaw-80K contains 80,049 artifact re- gions across 14,289 rendered images

## Abstract

3D Gaussian Splatting (3DGS) enables photorealistic novel- view synthesis with real-time rendering, yet fine-grained di- agnosis of its rendering artifacts remains challenging. These artifacts are typically spatially localized and exhibit diverse degradation patterns, making them difficult to character- ize using global quality metrics and image-level assessment benchmarks, which provide no explicit indication of where they occur or what types of artifacts they represent. To ad- dress this limitation, we introduce GaussFlaw-80K, the first large-scale dataset for fine-grained perceptual diagnosis in 3DGS. Constructed through a model-assisted human-in-the- loop pipeline, GaussFlaw-80K contains 80,049 artifact re- gions across 14,289 rendered images, each annotated with a pixel-level mask and one of eight flaw labels spanning ge- ometric, blending, and photometric degradations. These an- notations turn 3DGS artifact diagnosis into an explicit and measurable task. Building on this dataset, we propose Ge- oCritic, a geometry-guided multi-task model that integrates RGB appearance with depth-derived cues to jointly localize artifacts and classify their flaw types. Beyond evaluation, we further demonstrate the downstream benefits of fine-grained diagnosis through three complementary targeted operations: Gaussian densification, attribute correction, and outlier prun- ing. Experiments show that GeoCritic achieves an AUC-J of 0.9507 and an Acc@1 of 80.08%, while diagnosis-guided re- finement improves all reported global and crop-level metrics across diverse datasets and 3DGS backbones.

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