Check Projection Against Image Points
A projected space corner misses its observed pixel by several pixels.
Check Projection Against Image Points explained visually
A projected space corner misses its observed pixel by several pixels. English captions are optional and start off. The complete transcript follows the written lesson.
What you will learn
A projected space corner misses its observed pixel by several pixels.
A practical question
A calibration program reports a plausible camera pose. Does it actually place the parking-space corners where they appear in the photo? Project several known world points through the estimated camera model and draw the results over the raw image.
The useful idea
For each point, measure how far the predicted pixel is from the observed corner. Look at the direction as well as the distance of that error. A uniform shift may suggest a wrong origin. Errors that grow toward the image edges may point to missing distortion or a poor calibration. A point hidden behind a car should not be treated as a reliable observed corner.
Work through the example
Keep some corners aside from the fitting step and inspect them too. If every check is also used to tune the model, it cannot tell us how well the mapping generalizes across the lot.
Limits and next step
Record the correspondences and overlays. Next, we'll capture the varied views needed to estimate the camera's parameters in the first place.
Worked example
Check held-out corners near and far from the lens, not only points used to estimate parameters.
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
Projection combines geometry and camera parameters; observed-versus-projected residuals reveal fit quality. A low average residual can hide a severe local error.
What is the main idea behind check projection against image points?
Projection combines geometry and camera parameters; observed-versus-projected residuals reveal fit quality.
How is it used in the example?
Check held-out corners near and far from the lens, not only points used to estimate parameters.
What limitation should be checked?
A low average residual can hide a severe local error.
Further reading
Programming Computer Vision with Python — Jan Erik Solem.
Original ToolsInVision teaching examples. Editorial approach · Suggest a correction
Accessible referenceLesson 20 transcript159 words · Video transcript · Matches the published narration and captionsOpen transcript
A calibration program reports a plausible camera pose. Does it actually place the parking-space corners where they appear in the photo? Project several known world points through the estimated camera model and draw the results over the raw image.
For each point, measure how far the predicted pixel is from the observed corner. Look at the direction as well as the distance of that error. A uniform shift may suggest a wrong origin. Errors that grow toward the image edges may point to missing distortion or a poor calibration. A point hidden behind a car should not be treated as a reliable observed corner.
Keep some corners aside from the fitting step and inspect them too. If every check is also used to tune the model, it cannot tell us how well the mapping generalizes across the lot.
Record the correspondences and overlays. Next, we'll capture the varied views needed to estimate the camera's parameters in the first place.