用神经网络生成可直接3D打印的结构，提升制造可行性。
Decoder Generates Manufacturable Structures: A Framework for 3D-Printable Object Synthesis
- 用解码器将抽象表示转为满足打印约束的几何结构
- 生成的零件制造成功率显著高于传统方法
- 适合需要快速设计可制造3D模型的工程师
本文提出一种基于解码器的新方法，用于生成适用于增材制造的可制造3D结构。我们构建了一个深度学习框架，将潜在表示解码为几何有效且可打印的物体，同时满足悬垂角度、壁厚和结构完整性等制造约束。实验表明，神经解码器能够学习从抽象表征到有效3D几何的复杂映射函数，生成的零件在制造可行性方面显著优于直接生成方法。我们在多种物体类别上验证了该方法，并成功实现了所生成结构的实际3D打印。
原文摘要 · Abstract (English)
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.
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