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Neural field-based 3D representations have recently been adopted in manyareas including SLAM systems. Current neural SLAM or online mapping systemslead to impressive results in the presence of simple captures, but they rely ona world-centric map representation as only a single neural field model is used.To define such a world-centric representation, accurate and static priorinformation 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 dynamicallychanges and it is subject to significant updates based on run-timeobservations. Particularly in the context of large-scale mapping, significantcamera pose drift is inevitable, necessitating the correction via loop closure.To overcome this limitation, we propose NEWTON, a view-centric mapping methodthat dynamically constructs neural fields based on run-time observation. Incontrast to prior works, our method enables camera pose updates using loopclosures and scene boundary updates by representing the scene with multipleneural fields, where each is defined in a local coordinate system of a selectedkeyframe. The experimental results demonstrate the superior performance of ourmethod over existing world-centric neural field-based SLAM systems, inparticular for large-scale scenes subject to camera pose updates.

NEWTON: Neural View-Centric Mapping for On-the-Fly Large-Scale SLAM