AssetField: Assets Mining and Reconfiguration in Ground Feature Plane Representation

Understand

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.