Processing options
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      Calibration - PIX4Dmatic

      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. 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.

      Access:
      • Click the Process Processing_button.png icon.
      • From the Menu bar, click Process > Calibrate...

      IN THIS ARTICLE

      Calibration
      Template
      Pipeline
      Image scale
      Keypoints
      Internals confidence
      Automatic ITPs
      Use depth maps (Optional)
      Reoptimization

      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.

      Calibration_template.png

      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 scenes.

      • Map: Generate 2D and 3D reconstructions of an area of interest using aerial images. This template was designed to address the need for accuracy in mapping terrain.

        General characteristics:

        • Default pipeline: Standard.
        • Typical Input: Nadir imagery.
        • Processing Speed: Slower.

      • 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.
      Tip: Reducing the Image scale will usually result in a slightly reduced accuracy because fewer keypoints will be extracted. On the other hand, it can help to calibrate datasets with blurry images or datasets containing homogenous areas. We recommend reducing Image scale:
      • 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.
      Information: Keypoints are computed with multiple image scales, including all image scales between the selected image scale and 1/8 image scale. For example, if 1/2 is selected, keypoints are computed with 1/2, 1/4, and 1/8 image scale.

      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.
      Information: When extracting keypoints, a score is assigned to each keypoint. Based on this score, the best keypoints are selected.

      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.

      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.

      Note: Using PIX4Dcatch data that contains supplemental LiDAR data can help eliminate GNSS and IMU drift. For more information: PIX4Dmatic: combined LiDAR and photogrammetry workflow.

      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.

      Reoptimization

      PIX4Dmatic_reoptimization.jpg

      Access: In the menu bar, click Process - Reoptimize Calibration...

      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 Calibrate step is completed.

      Toggle on Rematch to compute more matches between images and optimize the internal and external camera parameters.

      Tip: Using the Reoptimization option reduces the processing time after applying changes to GCPs.

      Such changes can be:

      • Adding GCPs marks.
      • Changing the position of existing GCPs marks.
      • Removing GCPs with marks.
      Important: The project is reoptimized only when all other Calibrate options remain unchanged. For example, when changing the Image scale or Keypoints, the Calibrate step must be re-run in place of Reoptimize.