用点云监督生成可编辑的3D模型，突破传统设计数据瓶颈
DreamCAD: Scaling Multi-modal CAD Generation using Differentiable Parametric Surfaces
- 将CAD模型表示为可微分的参数化曲面，直接从点云生成可编辑边界表示
- 在100万条文本描述数据上训练，几何保真度领先，用户偏好超75%
- 适用于需要快速生成高质量可修改3D设计的工业设计与AI辅助建模场景
计算机辅助设计依赖结构化且可编辑的几何表示，但现有生成方法受限于小规模带标注的设计历史或边界表示（BRep）数据。与此同时，数百万未标注的3D网格尚未被利用，制约了大规模CAD生成的发展。为此，我们提出DreamCAD，一种多模态生成框架，可直接从点级监督生成可编辑的BReps，无需特定CAD标注。DreamCAD将每个BRep表示为一组参数化曲面（如贝塞尔曲面），并使用可微分三角化方法生成网格，实现大规模3D数据集上的训练，同时重建连通且可编辑的表面。此外，我们构建了目前最大的CAD描述数据集CADCap-1M，包含超过100万条由GPT-5生成的描述，以推动文本到CAD研究。DreamCAD在ABC和Objaverse基准测试中，在文本、图像和点模态下均达到最先进性能，几何保真度高，用户偏好超过75%。代码与数据集将公开。
原文摘要 · Abstract (English)
Computer-Aided Design (CAD) relies on structured and editable geometric representations, yet existing generative methods are constrained by small annotated datasets with explicit design histories or boundary representation (BRep) labels. Meanwhile, millions of unannotated 3D meshes remain untapped, limiting progress in scalable CAD generation. To address this, we propose DreamCAD, a multi-modal generative framework that directly produces editable BReps from point-level supervision, without CAD-specific annotations. DreamCAD represents each BRep as a set of parametric patches (e.g., Bézier surfaces) and uses a differentiable tessellation method to generate meshes. This enables large-scale training on 3D datasets while reconstructing connected and editable surfaces. Furthermore, we introduce CADCap-1M, the largest CAD captioning dataset to date, with 1M+ descriptions generated using GPT-5 for advancing text-to-CAD research. DreamCAD achieves state-of-the-art performance on ABC and Objaverse benchmarks across text, image, and point modalities, improving geometric fidelity and surpassing 75% user preference. Code and dataset will be publicly available.
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