Emergent Outlier View Rejection in Visual Geometry Grounded Transformers
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arXiv
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| Format: | Preprint |
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2025
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| author | Han, Jisang Hong, Sunghwan Jung, Jaewoo Jang, Wooseok An, Honggyu Wang, Qianqian Kim, Seungryong Feng, Chen |
| author_facet | Han, Jisang Hong, Sunghwan Jung, Jaewoo Jang, Wooseok An, Honggyu Wang, Qianqian Kim, Seungryong Feng, Chen |
| contents | Reliable 3D reconstruction from in-the-wild image collections is often hindered by "noisy" images-irrelevant inputs with little or no view overlap with others. While traditional Structure-from-Motion pipelines handle such cases through geometric verification and outlier rejection, feed-forward 3D reconstruction models lack these explicit mechanisms, leading to degraded performance under in-the-wild conditions. In this paper, we discover that the existing feed-forward reconstruction model, e.g., VGGT, despite lacking explicit outlier-rejection mechanisms or noise-aware training, can inherently distinguish distractor images. Through an in-depth analysis under varying proportions of synthetic distractors, we identify a specific layer that naturally exhibits outlier-suppressing behavior. Further probing reveals that this layer encodes discriminative internal representations that enable an effective noise-filtering capability, which we simply leverage to perform outlier-view rejection in feed-forward 3D reconstruction without any additional fine-tuning or supervision. Extensive experiments on both controlled and in-the-wild datasets demonstrate that this implicit filtering mechanism is consistent and generalizes well across diverse scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_04012 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Emergent Outlier View Rejection in Visual Geometry Grounded Transformers Han, Jisang Hong, Sunghwan Jung, Jaewoo Jang, Wooseok An, Honggyu Wang, Qianqian Kim, Seungryong Feng, Chen Computer Vision and Pattern Recognition Reliable 3D reconstruction from in-the-wild image collections is often hindered by "noisy" images-irrelevant inputs with little or no view overlap with others. While traditional Structure-from-Motion pipelines handle such cases through geometric verification and outlier rejection, feed-forward 3D reconstruction models lack these explicit mechanisms, leading to degraded performance under in-the-wild conditions. In this paper, we discover that the existing feed-forward reconstruction model, e.g., VGGT, despite lacking explicit outlier-rejection mechanisms or noise-aware training, can inherently distinguish distractor images. Through an in-depth analysis under varying proportions of synthetic distractors, we identify a specific layer that naturally exhibits outlier-suppressing behavior. Further probing reveals that this layer encodes discriminative internal representations that enable an effective noise-filtering capability, which we simply leverage to perform outlier-view rejection in feed-forward 3D reconstruction without any additional fine-tuning or supervision. Extensive experiments on both controlled and in-the-wild datasets demonstrate that this implicit filtering mechanism is consistent and generalizes well across diverse scenarios. |
| title | Emergent Outlier View Rejection in Visual Geometry Grounded Transformers |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.04012 |