Abstract:Recent advances in 3D Gaussian Splatting (3DGS) have enabled impressive real-time photorealistic rendering. However, conventional training pipelines inherently assume full multi-view consistency among input images, which makes them sensitive to distractors that violate this assumption and cause visual artifacts. In this work, we revisit an underexplored aspect of 3DGS: its inherent ability to suppress inconsistent signals. Building on this insight, we propose PDF-GS (Progressive Distractor Filtering for Robust 3D Gaussian Splatting), a framework that amplifies this self-filtering property through a progressive multi-phase optimization. The progressive filtering phases gradually remove distractors by exploiting discrepancy cues, while the following reconstruction phase restores fine-grained, view-consistent details from the purified Gaussian representation. Through this iterative refinement, PDF-GS achieves robust, high-fidelity, and distractor-free reconstructions, consistently outperforming baselines across diverse datasets and challenging real-world conditions. Moreover, our approach is lightweight and easily adaptable to existing 3DGS frameworks, requiring no architectural changes or additional inference overhead, leading to a new state-of-the-art performance. The code is publicly available at this https URL.
| Comments: | Accepted to CVPR Findings 2026. Project Page: this https URL |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2604.12580 [cs.CV] |
| (or arXiv:2604.12580v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2604.12580 arXiv-issued DOI via DataCite |
From: Kangmin Seo [view email]
[v1]
Tue, 14 Apr 2026 11:03:55 UTC (9,778 KB)
[v2]
Thu, 16 Apr 2026 03:28:23 UTC (12,976 KB)
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