In this paper, we showcase the effectiveness of optimizing monocular camera
poses as a continuous function of time. The camera poses are represented using
an implicit neural function which maps the given time to the corresponding
camera pose. The mapped camera poses are then used for the downstream tasks
where joint camera pose optimization is also required. While doing so, the
network parameters -- that implicitly represent camera poses -- are optimized.
We exploit the proposed method in four diverse experimental settings, namely,
(1) NeRF from noisy poses; (2) NeRF from asynchronous Events; (3) Visual
Simultaneous Localization and Mapping (vSLAM); and (4) vSLAM with IMUs. In all
four settings, the proposed method performs significantly better than the
compared baselines and the state-of-the-art methods. Additionally, using the
assumption of continuous motion, changes in pose may actually live in a
manifold that has lower than 6 degrees of freedom (DOF) is also realized. We
call this low DOF motion representation as the \emph{intrinsic motion} and use
the approach in vSLAM settings, showing impressive camera tracking performance.

优化时间连续的单目相机姿态的有效性，采用隐式神经函数表示相机姿态，用于下游任务的联合相机姿态优化，提出的方法在四个实验设置中表现显著优于其他基准和现有方法，同时认识到连续运动中的姿态变化可能存在低于 6 个自由度的流形，称之为内在运动，在 vSLAM 设置中展示出强大的相机追踪性能。