Learning Shared Object Prototypes for Compositional 3D Gaussian Splatting

Existing 3D Gaussian Splatting (3DGS) methods typically reconstruct each scene independently, entangling recurring objects with their original scene configurations and hindering their direct reuse and recombination. We present a prototype-based compositional 3DGS representation that learns globally shared Gaussian prototypes across multiple scenes. Given posed multi-view images with externally provided object masks and category labels, our method learns a canonical prototype for each recurring object category, while representing each object instance with a lightweight instance-specific similarity transformation. Each scene is then constructed by instantiating and composing the shared prototypes according to its scene-specific configuration. To address the strong coupling between prototype parameters and instance transformations, we introduce an alternating optimization strategy with a fixed canonical reference and prototype regularization. Experiments on GSO and OCTScenes show that our learned prototypes capture the geometry and appearance shared by recurring objects, maintain high-quality novel-view synthesis, and enable the instantiation and recombination of objects to construct new scene configurations.