PartSDF: Part-Based Implicit Neural Representation for Composite 3D Shape Parametrization and Optimization
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866914101711601664 |
|---|---|
| author | Talabot, Nicolas Clerc, Olivier Demirtas, Arda Cinar Goujon, Alexis Le, Hieu Oner, Doruk Fua, Pascal |
| author_facet | Talabot, Nicolas Clerc, Olivier Demirtas, Arda Cinar Goujon, Alexis Le, Hieu Oner, Doruk Fua, Pascal |
| contents | Accurate 3D shape representation is essential in engineering applications such as design, optimization, and simulation. In practice, engineering workflows require structured, part-based representations, as objects are inherently designed as assemblies of distinct components. However, most existing methods either model shapes holistically or decompose them without predefined part structures, limiting their applicability in real-world design tasks. We propose PartSDF, a supervised implicit representation framework that explicitly models composite shapes with independent, controllable parts while maintaining shape consistency. Thanks to its simple but innovative architecture, PartSDF outperforms both supervised and unsupervised baselines in reconstruction and generation tasks. We further demonstrate its effectiveness as a structured shape prior for engineering applications, enabling precise control over individual components while preserving overall coherence. Code available at https://github.com/cvlab-epfl/PartSDF. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_12985 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PartSDF: Part-Based Implicit Neural Representation for Composite 3D Shape Parametrization and Optimization Talabot, Nicolas Clerc, Olivier Demirtas, Arda Cinar Goujon, Alexis Le, Hieu Oner, Doruk Fua, Pascal Computer Vision and Pattern Recognition Artificial Intelligence Accurate 3D shape representation is essential in engineering applications such as design, optimization, and simulation. In practice, engineering workflows require structured, part-based representations, as objects are inherently designed as assemblies of distinct components. However, most existing methods either model shapes holistically or decompose them without predefined part structures, limiting their applicability in real-world design tasks. We propose PartSDF, a supervised implicit representation framework that explicitly models composite shapes with independent, controllable parts while maintaining shape consistency. Thanks to its simple but innovative architecture, PartSDF outperforms both supervised and unsupervised baselines in reconstruction and generation tasks. We further demonstrate its effectiveness as a structured shape prior for engineering applications, enabling precise control over individual components while preserving overall coherence. Code available at https://github.com/cvlab-epfl/PartSDF. |
| title | PartSDF: Part-Based Implicit Neural Representation for Composite 3D Shape Parametrization and Optimization |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2502.12985 |