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。

神经隐式表示中的单目相机连续姿态

Continuous Pose for Monocular Cameras in Neural Implicit Representation

This paper presents a framework that combines traditional keypoint-based
camera pose optimization with an invertible neural rendering mechanism. Our
proposed 3D scene representation, Nerfels, is locally dense yet globally
sparse. As opposed to existing invertible neural rendering systems which
overfit a model to the entire scene, we adopt a feature-driven approach for
representing scene-agnostic, local 3D patches with renderable codes. By
modelling a scene only where local features are detected, our framework
effectively generalizes to unseen local regions in the scene via an optimizable
code conditioning mechanism in the neural renderer, all while maintaining the
low memory footprint of a sparse 3D map representation. Our model can be
incorporated to existing state-of-the-art hand-crafted and learned local
feature pose estimators, yielding improved performance when evaluating on
ScanNet for wide camera baseline scenarios.

本文提出了一个框架，它将传统的基于关键点的相机姿态优化与可逆神经渲染机制相结合。我们提出的 3D 场景表示 Nerfels 在本地密集而全局稀疏。通过仅在检测到本地特征的位置建模场景，我们的框架通过神经渲染器中的可优化代码调节机制有效地概括了场景中的未见局部区域，同时保持了稀疏 3D 地图表示的低内存占用。该模型可与现有的最先进的手工制作和学习本地特征姿态估计器相结合，提高在 ScanNet 宽摄像机基线场景评估时的性能。