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Roger Mueller’s homemade scanner turns a sequence of 2D photographs into a 3D coin model. It does not measure depth with the camera: a stepper-driven carriage moves the coin along one axis while partially blocked white light casts a profile shadow, and software combines the images into a point cloud and mesh. The approach is a practical maker project, but its results depend on lighting and post-processing, and the project does not report a numerical scan resolution.
How does the scanner make a 3D model from 2D photos?
The scanner uses light sectioning. A white light source is partially blocked so it casts a shadow across the coin. As the carriage moves the coin along the X axis, the camera records successive views of the changing surface profile. The images supply shape information across the other dimensions; no single frame contains a complete 3D scan.
Mueller’s design favors a single mechanical axis as a comparatively straightforward homemade build. He notes that faster and better methods exist, but describes this arrangement as easier to implement. The project’s examples include Swiss, US, and euro coins, rather than a controlled test covering all coin materials or finishes. Mueller’s project page; Hackster.io’s report.
What hardware does the build use?
The project combines a Raspberry Pi 3B+ and Raspberry Pi HQ Camera with a 28YBJ-48 geared stepper motor, ULN2003 driver, LEDs, and linear rails and bearings. Most of the prototype housing is 3D printed. The camera also needs a suitable lens: Mueller tested two CCTV lenses and chose one with decent macro capability.
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The Raspberry Pi High Quality Camera was announced on 30 April 2020 with a 12.3-megapixel Sony IMX477 sensor and interchangeable C- and CS-mount lens support. Those specifications do not make every lens suitable for coin photography: check mount compatibility, close-focus capability, focus range, and working distance for the intended setup. Raspberry Pi’s announcement listed a $50 launch price for the camera module and lenses starting at $25; those are historical launch prices, not current retail quotes. Raspberry Pi’s announcement.
Mueller’s undated project page estimates that the parts, including the Pi, camera, and lens, cost “a little over $100.” Treat that as the author’s estimate for the build, not a current parts budget; availability and prices may differ now.
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How are the photographs processed?
- Capture a sequence: a Python script controls image capture as the coin is moved along the carriage.
- Generate a point cloud: a separate Python program processes the sequence into a set of 3D points.
- Create a mesh: import the point cloud into MeshLab or CloudCompare to form a surface.
- Refine the result: use Blender for additional processing. Mueller presents these scans as a starting point for reverse-engineering coins into CAD surfaces, a task that would require more specialized software.
The camera images therefore are not a finished 3D model by themselves. The reconstruction and cleanup stages are part of the workflow.
Why does lighting affect coverage and detail?
Lighting angle is a tradeoff. A larger incidence angle can improve vertical resolution, but if the light is too steep, some parts of the coin may remain unlit and go unscanned. The project uses adjustable lighting, and its carriage is moved manually. Mueller’s guidance is that “a good compromise is required in order to capture a maximum of the surface in one pass.”
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In practice, the goal is not simply to maximize the angle: the shadow must provide a useful profile while enough of the surface remains illuminated for the image sequence. Poor coverage at capture time cannot be reliably recovered by meshing alone.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What are the scanner’s limitations?
- Relief depth: Mueller reports that very shallow or deep relief can be difficult to capture accurately.
- Color and surface variation: color variation can introduce image noise and produce rough-looking surfaces.
- Resolution is not quantified: although the project is framed as high-resolution and provides sample scans, it gives no numeric resolution or controlled accuracy benchmark.
- Not a numismatic assessment: a model depicts captured shape; the project does not establish coin authenticity, grade, or market value.
These constraints mean the sample models demonstrate the method on several coins, not guaranteed performance across every coin design, relief, finish, or lighting condition. The project does not publish numerical speed or accuracy comparisons with other scanning methods.
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