DrawingsDreamer: A Unified Multi-View Engineering Drawings Generation Model

arXiv:2609.35242 · cs.CV · Submitted 2026-09-28 · Read on arXiv

cs.CV

Submitted: 2026-09-28

Updated: 2026-09-28

Terminology

• Alignment — 18× in this paper · explained in 1 episode
• Accuracy — 12× in this paper · explained in 1 episode
• Fidelity — 12× in this paper · explained in 2 episodes
• Tokenization — 11× in this paper · explained in 2 episodes
• Computer-Aided Design — 8× in this paper
• Point Clouds — 8× in this paper · explained in 2 episodes
• Chamfer Distance — 6× in this paper
• Core — 6× in this paper · explained in 2 episodes
• Generative Model — 5× in this paper · explained in 2 episodes
• Scalable Vector Graphics — 5× in this paper
• In-Context Learning — 4× in this paper · explained in 4 episodes
• Quantization — 4× in this paper · explained in 12 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Computational overhead — 3× in this paper · explained in 2 episodes
• Diffusion Model — 3× in this paper · explained in 8 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Generative Models — 3× in this paper · explained in 2 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Adaptation — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Failure Modes — 2× in this paper · explained in 3 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Hallucination — 2× in this paper · explained in 4 episodes
• Institutional Review Board — 2× in this paper
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Unified Framework — 2× in this paper · explained in 5 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Guided attention — 1× in this paper · explained in 2 episodes
• Jensen-Shannon Divergence — 1× in this paper
• Low-Rank Adaptation (LoRA) — 1× in this paper · explained in 9 episodes
• Maximum Mean Discrepancy — 1× in this paper
• Parameter-Efficient Fine-Tuning — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes
• Structural Reasoning — 1× in this paper · explained in 2 episodes
• Vision-language models — 1× in this paper · explained in 2 episodes

Sources

• GPT-4 Technical Report
• FIGR: Few-shot Image Generation with Reptile
• Photo2CAD: Automated 3D solid reconstruction from 2D drawings using OpenCV
• DreamCAD: Scaling Multi-modal CAD Generation using Differentiable Parametric Surfaces
• HiDiGen: Hierarchical Diffusion for B-Rep Generation with Explicit Topological Constraints
• LLaMA: Open and Efficient Foundation Language Models
• Wan: Open and Advanced Large-Scale Video Generative Models
• Qwen3 Technical Report
• Text2CAD: Text to 3D CAD Generation via Technical Drawings

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