Researchdiffusion models3d printingdinov2toolpath generation
Image2Gcode Generates Printer-Ready G-Code From Images
8.0
Researchers at Carnegie Mellon University publish Image2Gcode on arXiv, an end-to-end diffusion-transformer framework that converts 2D sketches or photographs into printer-ready G-code without CAD or slicing. Trained on the Slice-100K dataset with a DinoV2 encoder and 1D U-Net denoiser, the model produces manufacturable toolpaths with 2.4% shorter travel distance, streamlining prototyping while lacking full 3D interlayer awareness.
Key Points
- 1Generates structured extrusion toolpaths directly from images using a diffusion-transformer architecture.
- 2Reduces multi-step CAD-to-slicer workflow, enabling sketches or photos to produce ready G-code.
- 3Allows rapid prototyping and repair without CAD expertise, though limited by 2D slice-only planning.
Scoring Rationale
Novel end-to-end image-to-G-code method with practical results; limited by 2D slice formulation and arXiv-only validation.
Sources
Public references used for this report.
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