arXiv (Cornell University)
PartSDF: Part-Based Implicit Neural Representation for Composite 3D Shape Parametrization and Optimization
February 2025 • Nicolas Talabot, Olivier Clerc, Arda Cinar Demirtas, Doruk Oner, Pascal Fua
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 t…