AutoGCPs algorithm automatically locates targets in images and detects their centers with pixel-level accuracy. It reduces the time needed for tie points marking.
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Supported use cases
Currently, AutoGCP supports:
- Three kinds of targets: square, diagonal, and AeroPoint.
- Automatic detection is currently supported only on RGB images.
- Both nadir and oblique flights.
Square |
Diagonal |
AeroPoint |
Best practices
AutoGCP algorithm is robust to variations in scale and rotation and can work on different types of ground control points. Optimal results are obtained following these guidelines:
- The targets on the ground should be placed sufficiently distant from each other. The spacing should be at least larger than the camera georeferencing horizontal uncertainty.
- Images should not be blurry, and the targets should be clean.
- The target should not be obstructed by any object above it nor by shadows.
- If obstruction cannot be avoided, more targets than necessary can be used, to increase the chances that a sufficient number of targets is available for re-calibration or re-optimization even if some are not detected.
- The target must be large enough in order to be visible in the images.
- Good image overlap makes the AutoGCPs results more reliable, with each target visible in multiple images.
- Only the targets in use should be visible. If unused targets are visible in the images, AutoGCPs can be misled.
AutoGCPs in Pix4D products
AutoGCP algorithm is implemented in:
- PIX4Dcloud Advanced, an online platform for drone mapping, progress tracking, and site documentation.
- PIX4Dengine, customizable photogrammetry reconstruction engine.
- PIX4Dmatic, photogrammetry software for corridor and large scale mapping.
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