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是有结构且重复的。传统的建模流水线通过保留一个存储唯一对象模板的资产库,以实现多样性和内存效率。鉴于这一观察,我们提出了AssetField,一种新颖的神经场景表示方法,它学习了一组物体感知地面特征平面来表示场景,通过无监督方式构建模板特征补丁的资产库。与现有方法需要对象掩码来查询用于对象编辑的空间点不同,我们的地面特征平面表示提供了鸟瞰视角中场景的自然可视化,允许对对象进行各种操作(如平移,复制,变形)以配置新场景。借助模板特征补丁,为拥有许多重复项的场景启用了群体编辑,以避免对对象个体进行重复操作。我们展示了AssetField不仅在新视图合成方面实现了具有竞争力的性能,而且还为新场景配置生成了逼真的渲染结果。