Calibration is the first step in processing a dataset and lays the foundation for the following steps. Default settings are provided and are generally the best place to start.
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Additionally, there are several options to choose from, enabling customization to suit a dataset with a particular set of variables or needs. Template, Pipeline, Image scale, Keypoints, Internals confidence, and Automatic Intersection Tie points are available for adjustment in this important step and can be utilized to improve calibration where applicable. This article will define each to inform of its function and help aid selection when needed.
- Click the Process
icon. - From the Menu bar, click Process > Calibration...
IN THIS ARTICLE
Calibration
During the Calibration step, the images and additional inputs, such as GCPs, will be used to perform the following tasks:
- Keypoints extraction: Identify specific features as keypoints in the images.
- Keypoints matching: Find which images have the same keypoints and match them.
- Camera model optimization: Calibrate the internal (focal length) and external parameters (orientation) of the camera.
- Geolocation GPS/GCP: Locate the model if geolocation information is provided.
Automatic Tie Points are created during this step and are the foundation for the next steps of processing.
Reoptimization
- In the menu bar, click Process > Calibration... > Reoptimization
- Or select the Reoptimization button
after enabling the Calibration step and click Start.
This process is used to reoptimize already computed internal and external camera parameters. It can be used when changes to GCPs are applied after the Calibration step is completed.
- Toggle on Rematch to compute more matches between images and optimize the internal and external camera parameters.
- Toggle on Compute relative confidence to compute the relative confidence of the generated ATPs and enable color by confidence. The default is set to off. This setting will have an impact on the processing time.
- Toggle on Vertex conversion to use marked geometry vertices to improve the Calibration.
Such changes can be:
- Adding GCPs marks.
- Changing the position of existing GCPs marks.
- Removing GCPs with marks.
Template
A Template is a general set of predefined settings to handle a particular type of dataset. Select the template that the dataset matches the most, a matching pipeline for the selected template will also populate the appropriate field.
The following templates are available for selection:
- Large scale and corridor (Default): Generate 2D and 3D reconstructions of large project areas with aerial imagery from both nadir and less oblique perspectives to include a mix of both perspectives. This is the default template and was designed to handle a majority of datasets efficiently and quickly.
General characteristics:
- Default pipeline: Scalable Standard.
- Typical Input: Nadir imagery.
- Processing Speed: Faster.
- Flat scene and low texture: Generate 2D and 3D reconstructions of subject matter containing relatively homogenous textures and flat terrain. Images should be captured above the area of interest and are best from the nadir perspective. This template was designed for agricultural applications.
General characteristics:
- Default pipeline: Low texture planar.
- Recommended Input: Nadir imagery.
- Processing Speed: Slower.
Note: The Flat scene and low texture template should not be used with oblique images. - Map: Generate 2D and 3D reconstructions of a project using aerial images. This template is similar to Large scale and corridor, but optimized for medium and small-scale areas of interest.
General characteristics:
- Default pipeline: Standard.
- Typical Input: Nadir imagery.
- Processing Speed: Slower.
Tip: For projects with 1000 images or less, it is recommended to process with the Map template instead of the Larce scale and corridor.
- Model: Generate 3D reconstructions of objects with oblique images. This template was designed to address the specific needs for reconstructing objects.
General characteristics:
- Default pipeline: Standard.
- Typical Input: Oblique imagery.
- Processing Speed: Slower.
- PIX4Dcatch: Generate 2D and 3D reconstructions of an area or object of interest. This template was designed to optimize the processing of PIX4Dcatch datasets, to include specific options created just for PIX4Dcatch projects.
For more information: How to process PIX4Dcatch datasets in PIX4Dmatic.
General characteristics:
- Default pipeline: Trusted location and orientation.
- Typical Input: Terrestrial imagery.
- Processing Speed: Faster.
- Interior scene: For interior projects captured with the PIX4Dcatch mobile application and calibrated with ITPs.
General characteristics:
- Default pipeline: Trusted location and orientation.
- Typical Input: Terrestrial interior imagery.
- Processing Speed: Faster.
Pipeline
Pipeline defines how the camera's internal and external parameters are optimized.
- Scalable standard (default): This calibration pipeline is a sequential pipeline that enhances image calibration for large datasets and fast processing.
- Standard: This calibration pipeline is similar to Scalable Standard, but it is more robust, requires more processing time, and uses more PC resources.
- Low texture planar: The calibration pipeline is intended for aerial nadir images with accurate geolocation and homogeneous or repetitive content of relatively flat terrain.
- Trusted location and orientation: This calibration pipeline is intended for projects with accurate relative location and IMU data. For example, images taken with PIX4Dcatch in an indoor or outdoor setting or images from RTK or PPK drones or devices. All images must include information about the camera's initial position and orientation.
Image scale
Defines the image size at which keypoints are extracted. It is possible to select:
- 1 (Original image scale): This is the recommended Image scale.
- 1/2 (Half image scale): This can be used to speed up processing and for cameras with very high resolution.
- 1/4 (Quarter image scale): This can be used to speed up processing to get a fast overview and assess the completeness of the project.
- 1/8 (Eighth image scale): This can be used to speed up processing to get a fast overview and assess the completeness of the project.
- To speed up processing.
- To get a fast overview and to assess the completeness of the project.
- For datasets containing blurry images.
- When processing datasets of flat and homogenous or repetitive and complex areas, such as trees, forests, and fields, because it can result in a higher number of calibrated images.
Keypoints
Allows to influence the number of extracted keypoints.
- Auto (Default): PIX4Dmatic automatically determines the optimal number of keypoints to extract.
- Custom: Directs PIX4Dmatic to identify a specific number of keypoints, image content permitting.
- Number of Keypoints: The targeted number of keypoints to be extracted per image.
Internals confidence
Defines how much the camera parameters (internals) can be recalculated and adjusted during the project calibration:
- Low (Default): Optimizes all the internal camera parameters.
- High: Forces the internal parameters to be close to the initial values. Recommended when:
- The difference between the initial and optimized camera parameters is higher than 5%.
- The calibrated project is warped or curved.
- Absolute: The initial parameters are not optimized, they are considered as fixed.
Use depth maps (PIX4Dcatch LiDAR datasets)
Use depth maps are available and activated by default for projects captured with LiDAR depth data, and can be toggled off if desired. When enabled, the depth maps generated with PIX4Dcatch are used for better calibration.
For more information: How to process PIX4Dcatch datasets in PIX4Dmatic.
Automatic ITPs (Optional)
Toggle on to generate and match structural lines and intersections between images. The default is set to off, however, when enabled ITPs can be used to help calibrate a scene with man-made structural elements.
For more information: Intersection Tie Points - PIX4Dmatic.
Compute relative confidence
Toggle on to compute the relative confidence of the generated ATPs and enable color by confidence. The default is set to off. This setting will have an impact on the processing time.
Vertex conversion
Toggle on to use marked geometry vertices to improve the Calibration. The default is set to off. This feature is related to the Vertex Tie points. For more information: Tie Points (GCPs, MTPs, and CPs) - PIX4Dmatic