Semantic Foam: Unifying Spatial and Semantic Scene Decomposition

2026-04-28

AI Summary

Existing 3D scene reconstruction methods struggle to meet the demands of interactive graphics applications for high-quality, cross-view consistent semantic segmentation. This work proposes a novel approach based on Radiant Foam—a voxelized Voronoi grid—by introducing an explicit semantic feature field at the cell level and, for the first time, directly incorporating spatial regularization into this field to achieve joint spatial-semantic decomposition. This design significantly enhances cross-view consistency and effectively mitigates artifacts caused by occlusions and inconsistent supervision. Experimental results demonstrate that the proposed method outperforms state-of-the-art approaches such as Gaussian Grouping and SAGA in both object-level semantic segmentation accuracy and consistency.

Abstract

Modern scene reconstruction methods, such as 3D Gaussian Splatting, enable photo-realistic novel view synthesis at real-time speeds. However, their adoption in interactive graphics applications remains limited due to the difficulty of interacting with these representations compared to traditional, human-authored 3D assets. While prior work has attempted to impose semantic decomposition on these models, significant challenges remain in segmentation quality and cross-view consistency.To address these limitations, we introduce Semantic Foam, which extends the recently proposed Radiant Foam representation to semantic decomposition tasks. Our approach leverages the inherent spatial structure of Radiant Foam's volumetric Voronoi mesh and augments it with an explicit semantic feature field defined at the cell level. This design enables direct spatial regularization, improving consistency across views and mitigating artifacts caused by occlusion and inconsistent supervision, which are common issues in point-based representations.Experimental results demonstrate that our method achieves superior object-level segmentation performance compared to state-of-the-art approaches such as Gaussian Grouping and SAGA.Project page: http://semanticfoam.github.io/

Problem

• semantic decomposition
• scene reconstruction
• view consistency
• 3D Gaussian Splatting
• interactive graphics

Innovation

• Semantic Foam
• Radiant Foam
• semantic decomposition
• spatial regularization
• 3D Gaussian Splatting