让3D打印结构更轻且自支撑，还能直接打印。
DensiCrafter: Physically-Constrained Generation and Fabrication of Self-Supporting Hollow Structures
- 用密度场优化生成中空结构，自动满足自支撑要求。
- 文本生成3D物体时材料质量减少43%，且保持形状精度。
- 无需改模型架构，可直接用于现有生成系统，适合工业设计。
3D生成模型虽能从多模态输入（如文本或图像）自动合成3D几何与纹理，但常忽略物理约束与可制造性。本文提出DensiCrafter，一种生成轻量化、自支撑3D中空结构的框架，通过优化密度场实现。从Trellis生成的粗粒度体素网格出发，将其视为连续密度场进行优化，并引入三种可微分、物理约束且无需仿真的损失项。质量正则化惩罚多余材料，受限优化域保留外表面。该方法可无缝集成至基于Trellis的预训练模型（如Trellis、DSO），无需修改架构。大量实验表明，在文本到3D任务中，材料质量最多降低43%；相比当前最优基线，结构稳定性提升且几何保真度高。真实3D打印实验证实，所生成设计可可靠制造并具备自支撑能力。
原文摘要 · Abstract (English)
The rise of 3D generative models has enabled automatic 3D geometry and texture synthesis from multimodal inputs (e.g., text or images). However, these methods often ignore physical constraints and manufacturability considerations. In this work, we address the challenge of producing 3D designs that are both lightweight and self-supporting. We present DensiCrafter, a framework for generating lightweight, self-supporting 3D hollow structures by optimizing the density field. Starting from coarse voxel grids produced by Trellis, we interpret these as continuous density fields to optimize and introduce three differentiable, physically constrained, and simulation-free loss terms. Additionally, a mass regularization penalizes unnecessary material, while a restricted optimization domain preserves the outer surface. Our method seamlessly integrates with pretrained Trellis-based models (e.g., Trellis, DSO) without any architectural changes. In extensive evaluations, we achieve up to 43% reduction in material mass on the text-to-3D task. Compared to state-of-the-art baselines, our method could improve the stability and maintain high geometric fidelity. Real-world 3D-printing experiments confirm that our hollow designs can be reliably fabricated and could be self-supporting.
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