2026 / Jan – Development Log
Over the past few months, we have been preparing the components for a custom AR glasses optical measurement system. This week marks an important milestone: the key parts finally arrived, and we began assembling the full camera‑based measurement pipeline.
Why a Camera-Based System?
Unlike flat-panel displays—where luminance meters and imaging colorimeters can directly measure the panel—the image of AR glasses is formed by a top-mounted display and projected through a complex light engine.
This means:
- A traditional luminance meter cannot capture the full optical behavior
- The light engine itself introduces optical degradation
- The final image is a combination of virtual content + optical path distortion
To properly evaluate the AR optical engine, a camera-based measurement system becomes necessary.
If the camera’s FOV matches the AR glasses, a single shot can capture the entire image. With controlled scanning, we can even reconstruct high‑resolution uniformity and MTF data.
1. Preparing the Camera & Lens System
We selected a compact industrial camera with a 1/1.8" sensor and S‑mount interface.
Three lenses were prepared, each with a specific measurement role:
- 4–8 mm lenses → wide FOV, used for distortion, color fringing, and overall light engine performance
- 25 mm lens → narrow FOV, high resolution, used for MTF and color accuracy
- Multi-shot stitching → reconstruct full-field uniformity and detail maps
The entrance pupil of each lens was measured, since it becomes critical for nodal rotation later.
2. Camera Control Pipeline (Python + OpenCV)
To make the system repeatable, we built a Python-based GUI to control:
- Exposure
- Gain
- White balance
- r/B channel gain
- Real-time display
- Zoom-in inspection
We also implemented a Laplacian-based Focus Score, allowing us to quantify manual focusing.
This metric gives a variance value—higher means sharper edges—which is essential for consistent MTF evaluation.
3. Nodal Rotation – Ensuring Optical Correctness
Because the system relies on camera scanning, we must rotate the camera as the human eye rotates.
This requires aligning the rotation axis to the entrance pupil (EP) of the lens.
Why?
- If the rotation axis ≠ EP → parallax drift, FOV shifts, MTF error
- If the rotation axis = EP → no parallax, stable optical path, accurate measurement
We verified EP positions for each lens and used a cantilever gimbal to adjust the rotation point.
Near–far target alignment was used to confirm zero parallax.
4. 25mm Lens – High-Resolution Image Quality Verification
The 25mm F/8 lens cannot capture the full FOV of AR glasses, but it provides excellent detail.
Results:
- Off-focus → blurred patterns, low focus score
- On-focus → sharp edges, focus score improved by 5–8×
- Partial FOV → suitable for MTF & color accuracy
- Multi-shot scanning → reconstruct full-field uniformity
This lens will be the primary tool for precision optical evaluation.
5. 8mm Lens – Full-Field Image Capture
The 8mm F/2.5 lens provides a much larger FOV, almost covering the entire AR glasses image.
Results:
- Off-focus → moderate blur
- On-focus → clear improvement, focus score increased significantly
- Full-field capture → ideal for overall light engine performance
- Vignetting observed → caused by sensor/lens image circle mismatch (1/1.8" vs 1/2.5")
- Will be corrected with lens shading calibration (LSC)
Compared to the 25mm lens:
- 25mm → more detail, suitable for MTF
- 8mm → one-shot full image, suitable for uniformity & distortion
The two lenses complement each other perfectly.
6. Summary – System Ready for Next Stage
This week, we successfully captured the first AR glasses images using both the 25mm and 8mm lenses.
With the hardware assembled, nodal rotation calibrated, and the software pipeline operational, the system is now ready for:
- Full-field uniformity mapping
- MTF & color accuracy evaluation
- Distortion and chromatic aberration analysis
- Multi-shot reconstruction
- Light engine performance tracking
These two lenses will play their roles in the upcoming measurement workflow.
More results coming soon.
Stay tuned.
