Stereo Camera Calibration: Why One Error Metric Is Not Enough

Stereo vision is an important component of real-world data capture for Physical AI, robotics, and multimodal learning.

However, capturing synchronized left and right images does not automatically guarantee accurate stereo geometry.

At Origin Data Lab , we have been evaluating the geometric consistency and calibration limitations of our stereo capture workflow.

In Technical Field Note #04 , we examine five important aspects of stereo camera calibration.

1. Epipolar Error Distribution

A single average error value cannot fully describe calibration quality.

Examining the distribution of epipolar errors helps identify residual misalignment and potential geometric inconsistencies.

2. Capture-Mode Consistency

Calibration parameters must correspond to the actual image capture configuration.

Changes in resolution, cropping, or camera operating mode can affect whether existing calibration parameters remain applicable.

3.…