Learning From Design Procedure To Generate CAD Programs for Data Augmentation
Learning From Design Procedure To Generate CAD Programs for Data Augmentation explores Augment CAD training data by prompting LLMs to generate programs conditioned on reference surfaces and modeling procedures, improving geometric diversity for industry-grade designs.. Commercial viability score: 7/10 in CAD Program Generation.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisSldprtNet: A Large-Scale Multimodal Dataset for CAD Generation in Language-Driven 3D DesignViability: 6.0
- Builds On ThisTowards Next-Generation LLM Training: From the Data-Centric PerspectiveViability: 4.0
- Prior WorkTowards High-Fidelity CAD Generation via LLM-Driven Program Generation and Text-Based B-Rep Primitive GroundingViability: 7.0
- Prior WorkDesign-Specification Tiling for ICL-based CAD Code GenerationViability: 7.0
- Prior WorkCADSmith: Multi-Agent CAD Generation with Programmatic Geometric Validation