通过视图图剪枝实现鲁棒的全局运动恢复结构（Structure-from-Motion, SfM）

中文摘要 AI 辅助

运动恢复结构（Structure-from-Motion, SfM）旨在从一组无序图像中估计相机位姿并重建三维结构。与增量式SfM相比，全局SfM基于由成对对应关系构建的视图图联合估计相机位姿，具备更优的可扩展性。然而，其性能对视觉模糊匹配导致的错误边高度敏感，可能引发错误的相机配准与重建伪影。本研究提出一种子图引导的视图图剪枝框架以实现鲁棒的全局SfM，核心思路是利用可靠子图的内部一致性识别并移除不可靠连接。具体而言，首先将视图图划分为局部一致的子图，在每个子图内执行全局SfM以获取可靠相机位姿；随后采用基于RANSAC的边剪枝操作，移除子图间的不一致边；最终在优化后的视图图上执行全局SfM。在模糊图像、序列图像及无序图像数据集上开展的大量实验表明，所提方法可提升全局SfM在挑战性场景下的鲁棒性；结合神经渲染的进一步评估显示，改进后的相机估计能生成更高质量的新视图合成结果。

英文摘要

Structure-from-Motion (SfM) aims to estimate camera poses and reconstruct 3D structures from a collection of unordered images. Compared with incremental SfM, global SfM achieves better scalability by jointly estimating camera poses based on a view graph constructed from pairwise correspondences. However, its performance is highly sensitive to erroneous edges caused by visually ambiguous matches, which may lead to incorrect camera registration and reconstruction artifacts. In this work, we propose a subgraph-guided view graph pruning framework for robust global SfM. Our key idea is to exploit the internal consistency of reliable subgraphs to identify and remove unreliable connections. Specifically, we first partition the view graph into locally consistent subgraphs and perform global SfM within each subgraph to obtain reliable camera poses. We then apply RANSAC-based edge pruning across subgraphs to remove inconsistent edges, and finally perform global SfM on the refined view graph. Extensive experiments on ambiguous, sequential, and unordered image datasets demonstrate that our method improves the robustness of global SfM under challenging conditions. Further evaluation with neural rendering shows that the improved camera estimation leads to higher-quality novel view synthesis results.

发表机构

• Hangzhou Dianzi University（杭州电子科技大学）
• State Key Lab of CAD&CG, Zhejiang University（浙江大学CAD&CG国家重点实验室）

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