arXiv:2605.01171v2 Announce Type: replace-cross Abstract: Despite recent progress, recovering parametric CAD construction sequences from geometric input, such as meshes or point clouds, is a key challenge for design and manufacturing, as existing CAD reconstruction and generation methods are largely restricted to difficult-to-edit formats like meshes or Breps or editable simple sketch-and-extrude pipelines and low-complexity datasets. We introduce CADFit, a hybrid optimization-based CAD reconstruction framework that recovers complex, editable CAD construction sequences from meshes by increment
Advances in AI, particularly in generative models and optimization, are enabling more sophisticated approaches to design automation and interoperability within engineering workflows.
Improving the ability to convert geometric data into editable CAD models can significantly streamline design, manufacturing, and R&D processes, reducing manual effort and accelerating product development cycles.
The barrier to converting non-parametric geometric data (like meshes) into editable, programmatic CAD sequences is lowered, making complex 3D models more accessible for iterative design and automated manufacturing.
- · Manufacturing sector
- · Product design firms
- · CAD software developers
- · Robotics and automation industries
- · Manual CAD conversion services
- · Companies reliant on proprietary, non-interoperable design formats
Faster iteration and optimization in product design and engineering workflows.
Increased adoption of customized and complex geometries in manufactured goods due to easier design-to-production pipelines.
Potential for fully autonomous design-to-manufacturing systems where AI agents handle concept generation through production instructions.
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Read at arXiv cs.LG