  Both indoor and outdoor environments are inherently structured and
repetitive. Traditional modeling pipelines keep an asset library storing unique
object templates, which is both versatile and memory efficient in practice.
Inspired by this observation, we propose AssetField, a novel neural scene
representation that learns a set of object-aware ground feature planes to
represent the scene, where an asset library storing template feature patches
can be constructed in an unsupervised manner. Unlike existing methods which
require object masks to query spatial points for object editing, our ground
feature plane representation offers a natural visualization of the scene in the
bird-eye view, allowing a variety of operations (e.g. translation, duplication,
deformation) on objects to configure a new scene. With the template feature
patches, group editing is enabled for scenes with many recurring items to avoid
repetitive work on object individuals. We show that AssetField not only
achieves competitive performance for novel-view synthesis but also generates
realistic renderings for new scene configurations.
