Neural Parts

Neural Parts represents a significant leap in 3D representation learning, moving beyond rigid geometric primitives like cuboids or ellipsoids to learnable, expressive 'parts.' Architecturally, it utilizes Invertible Neural Networks (INNs) to define a homeomorphism between a unit sphere and a target part, allowing for complex deformations while maintaining a valid manifold structure. In the 2026 market landscape, Neural Parts is positioned as a foundational framework for semantic 3D decomposition, enabling AI systems to not only reconstruct objects but to understand their modular components. This is critical for industrial design automation and robotic manipulation, where understanding the 'handle' of a tool versus its 'base' is essential. The system achieves superior reconstruction accuracy compared to traditional methods by leveraging a predicted global transformation and a localized per-part deformation field. Its technical architecture is highly valued in R&D environments for creating interpretable digital twins that can be easily edited or simulated in physics engines, filling the gap between raw point clouds and structured CAD models.