前馈式三维重建的最新进展使得模型能够仅从图像流中恢复密集的场景表示和相机运动。然而，此类预测在长轨迹上容易产生不一致性，特别是在具有重复结构、弱纹理以及动态物体或人物的复杂环境中。缓解这些挑战的一种方法是使用全向相机，它提供宽广的空间覆盖范围并捕获更丰富的视觉信息。然而，大多数模型不支持360度图像，或者需要额外的微调。为了结合这两个方面，我们提出了 RIGOR：一种针对重力对齐的全向视频的大规模重建流程，该流程保留了冻结的前馈透视骨干网络，并将每个全景图利用为四视图虚拟 rig。rig 结构用于检测并修复局部不一致的预测，通过循环四视图一致性检索回环闭合，并在全局优化之前对候选重访点进行几何验证。经过验证的约束驱动 Sim(3) 位姿图，以校正序列中累积的旋转、平移和尺度漂移。我们证明，所提出的一致性机制在具有挑战性的建筑工地序列上，相较于前馈基线方法，同时提高了轨迹精度和重建几何质量。代码可通过以下链接获取：https://github.com/TangentH/RIGOR。

Recent developments in feed-forward 3D reconstruction resulted in models which can recover dense scene representations and camera motion solely from an image stream. However, such predictions are prone to becoming inconsistent over long trajectories, specifically in demanding environments with repetitive structures, weak textures and dynamic objects or people. One way to mitigate those challenges is to use an omnidirectional camera, which provides wide spatial coverage and captures richer visual information. Yet, the majority of models do not offer support for 360-degree imagery or require additional fine-tuning. To bridge these two aspects, we present RIGOR: a large-scale reconstruction pipeline for gravity-aligned omnidirectional videos that retains a frozen feed-forward perspective backbone and exploits each panorama as a four-view virtual rig. The rig structure is used to detect and repair locally inconsistent predictions, to retrieve loop closures through cyclic four-view consensus, and to geometrically verify candidate revisits before global optimization. Verified constraints drive a Sim(3) pose graph that corrects accumulated rotation, translation, and scale drift along the sequence. We demonstrate that the proposed consistency mechanisms improve both trajectory accuracy and reconstructed geometry over a feed-forward baseline on challenging construction-site sequences. The code is made available under this link: https://github.com/TangentH/RIGOR.