DreamCAD: Scaling Multi-modal CAD Generation using Differentiable Parametric Surfaces
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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Mohammad Sadil Khan
claimed_verified
2026-03-10T09:45:31.708Z
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Mohammad Sadil Khan
SadilKhan
user
SadilKhan
69af2a5db3ab31dee1281aee
Muhammad Usama
claimed_verified
2026-03-25T10:51:46.629Z
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Muhammad Usama
MUsama100
user
MUsama100
69af2a5db3ab31dee1281aef
Rolandos Alexandros Potamias
69af2a5db3ab31dee1281af0
Didier Stricker
69af2a5db3ab31dee1281af1
Muhammad Zeshan Afzal
69af2a5db3ab31dee1281af2
Jiankang Deng
69af2a5db3ab31dee1281af3
Ismail Elezi
DreamCAD: Scaling Multi-modal CAD Generation using Differentiable Parametric Surfaces
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.
DreamCAD is a multi-modal generative framework that creates editable BReps from point-level supervision using parametric patches and differentiable tessellation, achieving superior geometric fidelity and user preference scores.
BReps parametric patches differentiable tessellation CAD captioning ABC benchmark Objaverse benchmark