Emergent Outlier View Rejection in Visual Geometry Grounded Transformers

• URL: http://arxiv.org/abs/2512.04012v1
• Date: Wed, 03 Dec 2025 17:48:25 GMT
• Title: Emergent Outlier View Rejection in Visual Geometry Grounded Transformers
• Authors: Jisang Han, Sunghwan Hong, Jaewoo Jung, Wooseok Jang, Honggyu An, Qianqian Wang, Seungryong Kim, Chen Feng,
• Abstract summary: "Noisy" images-irrelevant inputs with little or no view overlap with others hinder Reliable 3D reconstruction from in-the-wild image collections.<n>Traditional Structure-from-Motion pipelines handle such cases through geometric verification and outlier rejection.<n>In this paper, we discover that the existing feed-forward reconstruction model, VGGT, can inherently distinguish distractor images.
• Score: 45.83800698097105
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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