Semantic Foam: Unifying Spatial and Semantic Scene Decomposition

Current generation scene reconstruction methods like 3D Gaussian Splatting are capable of producing photo-realistic novel view synthesis at real-time speeds, yet see only limited adoption in many practical graphics applications.One significant contributing factor to this gap is the difficulty of interacting with and editing these representations in comparison to classic human-authored 3D assets.While work has been done to impose semantic decomposition onto these representations, there are still significant limitations in the quality and consistency of these segmentations.We address this by proposing a semantically decomposed variant of the recently introduced Radiant Foam method.Our approach, Semantic Foam, combines the natural spatial volumetric decomposition provided by Radiant Foam's Voronoi mesh with an explicit semantic feature field parameterized on the cells.The explicit mesh structure enables direct spatial regularization that prevents artifacts caused by inconsistent supervision across views or occlusion, which affect similar approaches for other point-based representations.We show that our method achieves superior performance on object-level segmentation compared to Gaussian Grouping and SAGA.