Reg-NF: Efficient Registration of Implicit Surfaces within Neural Fields
Stephen Hausler David Hall Sutharsan Mahendren Peyman Moghadam
Neural fields, coordinate-based neural networks, have recently gained
popularity for implicitly representing a scene. In contrast to classical
methods that are based on explicit representations such as point clouds, neural
fields provide a continuous scene representation able to represent 3D geometry
and appearance in a way which is compact and ideal for robotics applications.
However, limited prior methods have investigated registering multiple neural
fields by directly utilising these continuous implicit representations. In this
paper, we present Reg-NF, a neural fields-based registration that optimises for
the relative 6-DoF transformation between two arbitrary neural fields, even if
those two fields have different scale factors. Key components of Reg-NF include
a bidirectional registration loss, multi-view surface sampling, and utilisation
of volumetric signed distance functions (SDFs). We showcase our approach on a
new neural field dataset for evaluating registration problems. We provide an
exhaustive set of experiments and ablation studies to identify the performance
of our approach, while also discussing limitations to provide future direction
to the research community on open challenges in utilizing neural fields in
unconstrained environments.