Recovering the relative 6-DoF pose between two image groups underlies cross-sequence relocalization and multi-camera rig odometry. Each group carries known intra-group geometry from visual odometry or rig calibration, and pretrained multi-view backbones already fuse such geometry into visual features. Yet current models treat all views as an unstructured set, leaving cross-group reasoning as the missing piece. We introduce \ours{}, which keeps the foundation model entirely frozen and adds three lightweight trainable modules to bridge the two groups: a perceiver resampler, a cross-group bridge with merged self-attention, and a multi-frame pose head. The trainable footprint totals about 32M parameters, under 6\% of the full model, and is supervised only by relative poses. Across four datasets that span indoor and outdoor simulation, real-world cross-season capture, and zero-shot sim-to-real transfer, \ours{} attains state-of-the-art accuracy on both tasks, while every baseline is retrained with its full original supervision. Code is available at https://github.com/WeiYuFei0217/G2G.

翻译：恢复两个图像组之间的相对六自由度位姿是跨序列重定位和多相机刚体里程计的基础。每个组通过视觉里程计或刚体标定携带已知的组内几何信息，预训练的多视角骨干网络已将此几何信息融合到视觉特征中。然而，现有模型将所有视图视为非结构化集合，缺少跨组推理这一关键环节。我们提出\ours{}，该方法在保持基础模型完全冻结的前提下，添加三个轻量级可训练模块以桥接两组：感知器重采样器、带合并自注意力的跨组桥接模块及多帧位姿预测头。可训练参数总计约3200万，不到完整模型的6%，且仅通过相对位姿进行监督。在涵盖室内外仿真、真实跨季节采集及零样本仿真到现实迁移的四个数据集上，\ours{}在两项任务中均达到最先进精度，而所有基线均使用完整原始监督重新训练。代码开源于https://github.com/WeiYuFei0217/G2G。