Internship at Datagroup
360° object-capture rig
The problem
Image-recognition models are only as good as their training data. For physical objects that means hundreds of photographs of the same object under controlled, repeatable angles and lighting. Shooting these by hand doesn’t scale: it takes hours per object, angles drift between shots, and the dataset ends up inconsistent in exactly the ways that hurt model quality.
The solution
A rig that turns dataset photography into an unattended process: the object sits on a motorized turntable while a camera steps through elevation positions on a lead-screw tower and tilts via a servo-driven pan module. Rotation, elevation and tilt together cover a full sphere of viewpoints, and every position is exactly reproducible. Press start, come back to a finished dataset. Mechanics, electronics, and control software are one integrated system that I designed and built myself.
What I did
- Developed and CAD-designed the complete mechanics, using 3D-printed structural parts and standard drive components.
- Built the drives for the turntable, Z-axis, and camera tilt with stepper motors, a servo, and a printed gearbox.
- Integrated and commissioned the control and power electronics, including drivers, limit switches, and power supply.
- Developed the Python control on the Raspberry Pi for motion coordination, Arduino control, and automatic camera triggering.
- Built a Python script to generate 3D renderings from STL and STEP files.
Results
The rig captured objects from every angle without any manual intervention and produced a complete training dataset, combining the real photographs with additional views rendered from the CAD models. That dataset was then used to train a model for an image-recognition application.
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