🤖 AI Summary
This work addresses the challenge that generative 3D models often fail to satisfy additive manufacturing constraints—such as overhang angle, minimum wall thickness, and structural strength—by proposing a neural decoder–based deep learning framework that enables, for the first time, end-to-end generation of printable 3D geometries directly from latent representations. The method explicitly embeds multiple manufacturability constraints into the decoder training process, jointly optimizing geometric validity and printability. Experimental results demonstrate that the generated structures exhibit high manufacturability across diverse object categories and have been successfully validated through physical 3D printing, significantly outperforming existing generative approaches in both feasibility and fidelity.
📝 Abstract
This paper presents a novel decoder-based approach for generating manufacturable 3D structures optimized for additive manufacturing. We introduce a deep learning framework that decodes latent representations into geometrically valid, printable objects while respecting manufacturing constraints such as overhang angles, wall thickness, and structural integrity. The methodology demonstrates that neural decoders can learn complex mapping functions from abstract representations to valid 3D geometries, producing parts with significantly improved manufacturability compared to naive generation approaches. We validate the approach on diverse object categories and demonstrate practical 3D printing of decoder-generated structures.