  Neural field-based 3D representations have recently been adopted in many
areas including SLAM systems. Current neural SLAM or online mapping systems
lead to impressive results in the presence of simple captures, but they rely on
a world-centric map representation as only a single neural field model is used.
To define such a world-centric representation, accurate and static prior
information about the scene, such as its boundaries and initial camera poses,
are required. However, in real-time and on-the-fly scene capture applications,
this prior knowledge cannot be assumed as fixed or static, since it dynamically
changes and it is subject to significant updates based on run-time
observations. Particularly in the context of large-scale mapping, significant
camera pose drift is inevitable, necessitating the correction via loop closure.
To overcome this limitation, we propose NEWTON, a view-centric mapping method
that dynamically constructs neural fields based on run-time observation. In
contrast to prior works, our method enables camera pose updates using loop
closures and scene boundary updates by representing the scene with multiple
neural fields, where each is defined in a local coordinate system of a selected
keyframe. The experimental results demonstrate the superior performance of our
method over existing world-centric neural field-based SLAM systems, in
particular for large-scale scenes subject to camera pose updates.
