提出新方法，用环一致性实现高鲁棒性全局相机位姿估计
Cycle-Sync: Robust Global Camera Pose Estimation through Enhanced Cycle-Consistent Synchronization
- 基于消息传递最小二乘法，强化环一致性信息建模
- 仅靠环一致性即可实现最优采样复杂度的精确恢复
- 适合需要高精度、无需捆绑调整的视觉定位场景
我们提出Cycle-Sync，一种用于估计相机位姿（旋转与位置）的鲁棒且全局化的框架。核心创新在于将原本用于群同步的消息传递最小二乘法（MPLS）改进为适用于相机位置估计的方法：强调环一致性信息，利用前迭代估计的距离重新定义环一致性，并引入Welsch型鲁棒损失。我们建立了目前最强的确定性精确恢复保证，证明仅依赖环一致性——无需相机间距离信息——即可达到当前已知最低采样复杂度。为进一步提升鲁棒性，引入可插拔的异常值剔除模块，灵感来自鲁棒子空间恢复；并将环一致性完全融入旋转同步的MPLS中。该全局方法无需捆绑调整。在合成与真实数据集上的实验表明，Cycle-Sync持续优于领先的位姿估计算法，包括包含捆绑调整的完整结构从运动（SfM）流程。
原文摘要 · Abstract (English)
We introduce Cycle-Sync, a robust and global framework for estimating camera poses (both rotations and locations). Our core innovation is a location solver that adapts message-passing least squares (MPLS) -- originally developed for group synchronization -- to camera location estimation. We modify MPLS to emphasize cycle-consistent information, redefine cycle consistencies using estimated distances from previous iterations, and incorporate a Welsch-type robust loss. We establish the strongest known deterministic exact-recovery guarantee for camera location estimation, showing that cycle consistency alone -- without access to inter-camera distances -- suffices to achieve the lowest sample complexity currently known. To further enhance robustness, we introduce a plug-and-play outlier rejection module inspired by robust subspace recovery, and we fully integrate cycle consistency into MPLS for rotation synchronization. Our global approach avoids the need for bundle adjustment. Experiments on synthetic and real datasets show that Cycle-Sync consistently outperforms leading pose estimators, including full structure-from-motion pipelines with bundle adjustment.
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