DreamCAD: Scaling Multi-modal CAD Generation using Differentiable Parametric Surfaces

2026-03-05

AI Summary

This work proposes DreamCAD, a multimodal generative framework that addresses the limited scalability of existing CAD generation methods, which are constrained by small annotated datasets and unable to leverage vast unlabeled 3D meshes. DreamCAD enables end-to-end editable BRep generation supervised solely by point clouds—eliminating the need for CAD-specific annotations—and supports diverse inputs including text, images, and point clouds. It represents BReps using parametric surfaces such as Bézier patches and employs differentiable tessellation to produce meshes, facilitating training on large-scale 3D data. The authors also introduce CADCap-1M, a million-scale dataset of CAD models paired with textual descriptions, to advance text-to-CAD generation research. Evaluated on ABC and Objaverse benchmarks, DreamCAD achieves state-of-the-art performance with significantly improved geometric fidelity and a user preference rate exceeding 75%.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

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., B\'ezier 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.

Problem

• CAD generation
• unannotated 3D meshes
• boundary representation
• scalable generative modeling
• multi-modal CAD

Innovation

• differentiable parametric surfaces
• BRep generation
• multi-modal CAD
• point-level supervision
• CAD captioning