  Neural implicit representation has attracted attention in 3D reconstruction
through various success cases. For further applications such as scene
understanding or editing, several works have shown progress towards object
compositional reconstruction. Despite their superior performance in observed
regions, their performance is still limited in reconstructing objects that are
partially observed. To better treat this problem, we introduce category-level
neural fields that learn meaningful common 3D information among objects
belonging to the same category present in the scene. Our key idea is to
subcategorize objects based on their observed shape for better training of the
category-level model. Then we take advantage of the neural field to conduct the
challenging task of registering partially observed objects by selecting and
aligning against representative objects selected by ray-based uncertainty.
Experiments on both simulation and real-world datasets demonstrate that our
method improves the reconstruction of unobserved parts for several categories.
