A critical obstacle preventing NeRF models from being deployed broadly in the
wild is their reliance on accurate camera poses. Consequently, there is growing
interest in extending NeRF models to jointly optimize camera poses and scene
representation, which offers an alternative to off-the-shelf SfM pipelines
which have well-understood failure modes. Existing approaches for unposed NeRF
operate under limited assumptions, such as a prior pose distribution or coarse
pose initialization, making them less effective in a general setting. In this
work, we propose a novel approach, LU-NeRF, that jointly estimates camera poses
and neural radiance fields with relaxed assumptions on pose configuration. Our
approach operates in a local-to-global manner, where we first optimize over
local subsets of the data, dubbed mini-scenes. LU-NeRF estimates local pose and
geometry for this challenging few-shot task. The mini-scene poses are brought
into a global reference frame through a robust pose synchronization step, where
a final global optimization of pose and scene can be performed. We show our
LU-NeRF pipeline outperforms prior attempts at unposed NeRF without making
restrictive assumptions on the pose prior. This allows us to operate in the
general SE(3) pose setting, unlike the baselines. Our results also indicate our
model can be complementary to feature-based SfM pipelines as it compares
favorably to COLMAP on low-texture and low-resolution images.

本文提出了一种名为 LU-NeRF 的方法，通过利用放松姿态配置的放松姿态配置来同时估计相机姿态和神经辐射场，通过局部到全局的优化和姿态同步等步骤， 在一般的 SE（3）姿态设置下，无需对姿态先验进行强制约束，相比先前无姿态配置进行的 NeRF 试验表现更佳，这也使得其为特征基于的 SfM 管道提供了互补的选择。