Read Reprojection Error Honestly
A report says the calibration error is 0.4 pixels.
Read Reprojection Error Honestly explained visually
A report says the calibration error is 0.4 pixels. English captions are optional and start off. The complete transcript follows the written lesson.
What you will learn
A report says the calibration error is 0.4 pixels.
A practical question
Our calibration gives a small average reprojection error, and the team wants to declare victory. Before we do, examine individual errors. One blurry board view near the edge might be badly fit while many easy center points keep the average low.
The useful idea
Reprojection error is the distance between an observed image corner and the pixel predicted by the camera model. Plot the errors by location and by calibration frame. Look for direction and clustering, not only one overall number. A low training fit does not prove that a distant parking bay is mapped accurately.
Work through the example
Hold out a few useful views or ground points and check them independently. If errors rise near the border, investigate distortion coverage. If one frame is unusual, revisit its corner detections and focus.
Limits and next step
Report the units in pixels and the image size. Next, we'll use a planar mapping to measure a parking region viewed at an angle.
Worked example
Inspect per-image and per-region errors as well as the overall average.
Quick check: test your understanding
Try it yourself: In the situation described above, explain the method and one reason its output might be misleading.
Check the reasoning
Reprojection error measures discrepancy between observed calibration points and points predicted by the fitted model. A small training reprojection error does not guarantee accurate real-world distance.
What is the main idea behind read reprojection error honestly?
Reprojection error measures discrepancy between observed calibration points and points predicted by the fitted model.
How is it used in the example?
Inspect per-image and per-region errors as well as the overall average.
What limitation should be checked?
A small training reprojection error does not guarantee accurate real-world distance.
Further reading
Programming Computer Vision with Python — Jan Erik Solem.
Original ToolsInVision teaching examples. Editorial approach · Suggest a correction
Accessible referenceLesson 23 transcript150 words · Video transcript · Matches the published narration and captionsOpen transcript
Our calibration gives a small average reprojection error, and the team wants to declare victory. Before we do, examine individual errors. One blurry board view near the edge might be badly fit while many easy center points keep the average low.
Reprojection error is the distance between an observed image corner and the pixel predicted by the camera model. Plot the errors by location and by calibration frame. Look for direction and clustering, not only one overall number. A low training fit does not prove that a distant parking bay is mapped accurately.
Hold out a few useful views or ground points and check them independently. If errors rise near the border, investigate distortion coverage. If one frame is unusual, revisit its corner detections and focus.
Report the units in pixels and the image size. Next, we'll use a planar mapping to measure a parking region viewed at an angle.