
Hi everyone,

I’ve been working on a project called ShapeScan, focused on extracting clean geometric outlines from photos of real-world objects.

The goal is to convert images into usable vector and fabrication-ready formats such as SVG, DXF and STL.

The pipeline currently includes several stages:

1. Image normalization

- color calibration
- automatic page detection
- perspective correction
- noise cleanup

2. Segmentation

- classical segmentation for simple scenes
- optional background removal
- experiments with larger visual models for more complex objects

3. Contour extraction

- mask → contour detection
- topology preservation (outer contour + holes)
- contour smoothing

4. Geometry conversion

- contours converted into paths
- export to:
  - SVG
  - DXF
  - STL (extruded)

One of the main challenges has been producing stable and manufacturable contours, especially for workflows such as laser cutting, CNC or CAD prototyping.

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Drawing Mode (in development)

I’m currently working on a new drawing mode designed specifically for hand-drawn sketches.

The idea is simple:

- the user draws shapes on a sheet of paper
- takes a photo of the sheet
- ShapeScan extracts the drawn outlines
- and converts them into clean SVG vector paths

This mode uses a different processing pipeline tuned for:

- pen/pencil drawings
- sketch noise cleanup
- outline extraction from hand-drawn lines

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I’m also experimenting with integrating larger vision models to improve segmentation robustness for more complex scenes.

The long-term goal is to combine object scanning + sketch extraction into a single pipeline that can convert physical shapes or drawings into fabrication-ready geometry.

I’d be very interested in feedback from people working with:

- segmentation
- contour extraction
- vectorization pipelines
- topology-preserving geometry extraction

Happy to discuss approaches or technical challenges.

[Project] Extracting vector geometry (SVG/DXF/STL) from photos + experimental hand-drawn sketch extraction