PartSDF: Part-Based Implicit Neural Representation for Composite 3D Shape Parametrization and Optimization
It addresses the need for controllable part-based 3D representations in engineering design, offering an incremental improvement over existing methods.
The paper tackles the problem of representing 3D shapes with structured, part-based components for engineering applications, proposing PartSDF, which outperforms baselines in reconstruction and generation tasks.
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.