Semantic Foam: Unifying Spatial and Semantic Scene Decomposition
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866916062247780352 |
|---|---|
| author | Sharafeldin, Amr Govindarajan, Shrisudhan Walker, Thomas Mikaeili, Aryan Rebain, Daniel Yi, Kwang Moo Tagliasacchi, Andrea |
| author_facet | Sharafeldin, Amr Govindarajan, Shrisudhan Walker, Thomas Mikaeili, Aryan Rebain, Daniel Yi, Kwang Moo Tagliasacchi, Andrea |
| contents | Modern scene reconstruction methods, such as 3D Gaussian Splatting, deliver photo-realistic novel view synthesis at real-time speeds, yet their adoption in interactive graphics applications has been limited. A major bottleneck is the difficulty of interacting with these representations compared to traditional, human-authored 3D assets. While previous research has attempted to impose semantic decomposition on these models, significant challenges remain regarding segmentation quality and consistency. To address this, we introduce Semantic Foam, extending the recently proposed Radiant Foam representations to semantic decomposition tasks. Our approach integrates the natural spatial volumetric decomposition of Radiant Foam's Voronoi mesh with an explicit semantic feature field parameterized at the cell level. This explicit structure enables direct spatial regularization, which prevents artifacts caused by occlusion or inconsistent supervision across views - common pitfalls for other point-based representations. Experimental results show that our method achieves comparable or superior object-level segmentation performance compared to state-of-the-art methods like Gaussian Grouping and SAGA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_26262 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Semantic Foam: Unifying Spatial and Semantic Scene Decomposition Sharafeldin, Amr Govindarajan, Shrisudhan Walker, Thomas Mikaeili, Aryan Rebain, Daniel Yi, Kwang Moo Tagliasacchi, Andrea Computer Vision and Pattern Recognition Modern scene reconstruction methods, such as 3D Gaussian Splatting, deliver photo-realistic novel view synthesis at real-time speeds, yet their adoption in interactive graphics applications has been limited. A major bottleneck is the difficulty of interacting with these representations compared to traditional, human-authored 3D assets. While previous research has attempted to impose semantic decomposition on these models, significant challenges remain regarding segmentation quality and consistency. To address this, we introduce Semantic Foam, extending the recently proposed Radiant Foam representations to semantic decomposition tasks. Our approach integrates the natural spatial volumetric decomposition of Radiant Foam's Voronoi mesh with an explicit semantic feature field parameterized at the cell level. This explicit structure enables direct spatial regularization, which prevents artifacts caused by occlusion or inconsistent supervision across views - common pitfalls for other point-based representations. Experimental results show that our method achieves comparable or superior object-level segmentation performance compared to state-of-the-art methods like Gaussian Grouping and SAGA. |
| title | Semantic Foam: Unifying Spatial and Semantic Scene Decomposition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.26262 |