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.

翻译：室内和室外环境本质上都具有结构性和重复性。传统建模流程维护一个存储独特对象模板的资产库，这在实践中既通用又内存高效。受此启发，我们提出AssetField，一种新颖的神经场景表示方法，它以无监督方式学习一组面向对象的地面特征平面来表示场景，并可从中构建存储模板特征块的资产库。与需要对象掩码来查询空间点以进行对象编辑的现有方法不同，我们的地面特征平面表示提供了鸟瞰视角下场景的自然可视化，允许对对象进行多种操作（例如平移、复制、变形）来配置新场景。利用模板特征块，可对具有大量重复元素的场景进行组编辑，避免对单个对象的重复操作。我们证明，AssetField不仅在新视角合成任务上取得了有竞争力的性能，还能为新场景配置生成逼真的渲染结果。