In this paper, we introduce NoPe-NeRF++, a novel local-to-global optimization algorithm for training Neural Radiance Fields (NeRF) without requiring pose priors. Existing methods, particularly NoPe-NeRF, which focus solely on the local relationships within images, often struggle to recover accurate camera poses in complex scenarios. To overcome the challenges, our approach begins with a relative pose initialization with explicit feature matching, followed by a local joint optimization to enhance the pose estimation for training a more robust NeRF representation. This method significantly improves the quality of initial poses. Additionally, we introduce global optimization phase that incorporates geometric consistency constraints through bundle adjustment, which integrates feature trajectories to further refine poses and collectively boost the quality of NeRF. Notably, our method is the first work that seamlessly combines the local and global cues with NeRF, and outperforms state-of-the-art methods in both pose estimation accuracy and novel view synthesis. Extensive evaluations on benchmark datasets demonstrate our superior performance and robustness, even in challenging scenes, thus validating our design choices.

翻译：本文提出NoPe-NeRF++，一种无需姿态先验训练神经辐射场（NeRF）的新型局部到全局优化算法。现有方法（尤其是仅关注图像内部局部关系的NoPe-NeRF）在复杂场景中常难以恢复精确相机姿态。为克服此局限，本方法首先通过显式特征匹配进行相对姿态初始化，随后执行局部联合优化以提升姿态估计质量，从而训练更鲁棒的NeRF表征。该方法显著改善了初始姿态质量。此外，我们引入融合几何一致性约束的全局优化阶段，通过光束法平差整合特征轨迹以进一步优化姿态，并整体提升NeRF质量。值得注意的是，本方法首次实现了局部与全局线索与NeRF的无缝融合，在姿态估计精度和新视角合成方面均优于现有先进方法。在基准数据集上的大量实验验证了本方法即使在挑战性场景中仍具有卓越性能与鲁棒性，从而证实了设计决策的有效性。