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

AssetField: Assets Mining and Reconfiguration in Ground Feature Plane Representation