用全局优化提升3D基础模型的重建精度，解决长序列漂移问题。
Glob3R: Global Structure-from-Motion with 3D Foundation Models
- 在冻结主干网络上加轻量匹配头，通过图像变形预测建立稀疏特征轨迹
- 通过滑动窗口传播轨迹和相对位姿，实现大规模场景的可扩展重建
- 结合全局运动平均与捆绑调整，显著提升姿态精度和几何一致性，适合大场景应用
近期的3D几何基础模型（如VGGT）可通过直接预测相机位姿和3D场景点实现鲁棒的前馈式3D重建。然而，其结果仍存在误差，且在长序列或无序图像集上通常需分块处理，易引入漂移与不一致。本文提出Glob3R，一种基于3D基础模型的全局SfM式重建方法。核心思想是显式优化前馈几何预测：在冻结的Pi3X主干上添加轻量级密集匹配头，预测选定参考帧与邻近视图间的图像形变；这些密集形变被转化为稀疏但可靠的多视图特征轨迹，提供全局优化的对应约束。进一步提出基于关键帧的滑动窗口关联策略，跨重叠窗口传播轨迹与相对位姿，实现可扩展重建。最后执行全局运动平均与捆绑调整，精修相机位姿，减少尺度不一致，恢复稠密场景几何。在室内、室外、大规模驾驶及无序SfM基准上的实验表明，Glob3R实现稳健准确的重建，持续优于前馈基础模型基线及近期可扩展方法，且比传统SfM更鲁棒。优化后的位姿也带来更高品质的神经渲染，验证了基础模型先验与全局几何优化结合的有效性。
原文摘要 · Abstract (English)
Recent 3D geometric foundation models, such as VGGT, provide robust feed-forward 3D reconstruction by directly predicting camera poses and 3D scene points from input images. However, their results remain inaccurate, and scaling them to long sequences or large unordered image sets typically requires chunk-wise processing, which can introduce drift and inconsistency. We present Glob3R, a global SfM-style reconstruction built on 3D foundation models. Our key idea is to explicitly optimize feed-forward geometric predictions. To this end, we augment a frozen Pi3X backbone with a lightweight dense matching head that predicts image warps between selected reference frames and neighboring views. These dense warps are converted into sparse but reliable multi-view feature tracks, which provide correspondence constraints for global optimization. We further introduce a keyframe-based sliding-window association strategy that propagates tracks and relative poses across overlapping windows, enabling scalable reconstruction. Finally, we perform global motion averaging and bundle adjustment to refine camera poses, reduce scale inconsistencies, and recover dense scene geometry. Extensive experiments on indoor, outdoor, large-scale driving, and unordered SfM benchmarks demonstrate that Glob3R achieves robust and accurate reconstruction. It consistently improves over feed-forward foundation-model baselines and recent scalable reconstruction methods, while being more robust than classical SfM pipelines. The refined poses also lead to higher-quality neural rendering, validating the benefit of combining foundation-model priors with global geometric optimization. Project page: https://junyuandeng.github.io/Glob3r
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