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      How to use Automatic Autotag Detection and Manual GCP Marking - PIX4Dcatch

      Coded Autotags can now be used directly in PIX4Dcatch, which are detected and marked automatically by the application. Users only need to load the Point Collection that contains the GCPs, allowing for PIX4Dcatch projects to achieve higher absolute accuracies leveraged by GCPs without the need for tedious manual marking.

      IN THIS ARTICLE

      Download Autotags for Auto-Detection
      Best Practices
      Automatic Autotag Detection
      Manual Marking of GCPs and Autotags

      Optimize Autotags

      Download Autotags for Auto-Detection

      Autotags are uniquely coded targets that the application can automatically detect in images. Autotags are ideal and commonly used for marking GCPs on the field. Autotags can be downloaded here: A4, US Letter.

      Tip: Autotags can be printed on a sticker and glued to a firm plate (e.g. thin plywood). This makes the Autotags rigid and durable. Some print shops also provide services for printing the Autotags on vinyl or plastic, which makes them more durable and water resistant.

      Best Practices

      Placing of Autotags/GCPs

      • The Autotags/GCPs should be placed on the ground at a sufficient distance from each other.
      • The images should not be blurry and the Autotags/GCPs should be clean.
      • The Autotags/GCP should not be obstructed by any object above them nor by shadows.
        Example: The screenshots below show potentially problematic Autotags/GCPs for detection.
         
              .    IMG_0936
      • The Autotag/GCPs must be large enough in order to be visible in the images.
        Tip: We recommend printing the Autotags on A4 paper size.
      • Good image overlap makes the automatic Autotag Detection results more reliable, with each Autotag visible in multiple images.
      • If unused Autotags are visible in the images, automatic Autotag Detection can be misleading.

      Number and Distribution of Autotags/GCPs

      The Autotags/GCPs should be evenly distributed in the area of interest. Imagine the area as a large table and the Autotags/GCPs as the legs that will support it. If all the "legs" are placed at the same location of the "table", then it will tilt. If the "legs" are homogeneously spread, then the "table" will be stable. Additionally, it is also recommended to place one Autotag/GCP in the center of the area to further increase the quality of the reconstruction.

      Important:
      • It is mandatory to use a minimum of three Autotags/GCPs to be able to use them in the photogrammetry process. To anchor your project with better absolute accuracy, it is recommended to use 5 to 10 Autotags/GCPs, which is also sufficient for large projects. More Autotags/GCPs do not contribute significantly to increasing accuracy and avoiding drifts.
      • The Autotags/GCPs should be placed evenly on the landscape to minimize the error in scale and orientation.
      • Do not place the Autotags/GCPs exactly at the edges of the area, as they will only be visible in a few images.
      • The minimum number of Autotags/GCPs required for a corridor project depends on the same factors as many other types of projects, including the relative accuracy of the image geolocation, the amount of image overlap, and the length and width of the corridor.
      • We recommend that you distribute your Autotags/GCPs so that they are offset from one another, regardless of the number of Autotags/GCPs that you include in your project. You can consider including a pair of GCPs at each end of the corridor in addition to the set of offset Autotags/GCPs that you collected along the length of the corridor.
      Example:

      corridor_mapping_1.png

      Automatic Autotag Detection

      Autotags can be automatically detected by PIX4Dcatch. The only requirement is to select the Point Collection before starting the capturing process.

      Important: The Point Collection's output coordinate reference system has to be a projected CRS.

      Automatic Autotag Detection - Summary

      • Display targets on the scene
      • Measure the location of the targets
      • Turn on the Autotag Detection option
      • Select your Point Collection
      • Start capture
      • Process project with GCPs

       

       

      Automatic Autotag Detection - Detailed Description

      To select the Point Collection, first visit the PIX4Dcatch_ToolsMenu.png Tools at the bottom and select Autotag Detection.

      Select the Point Collection to use for the automatic tag detection.

      Important: Matching the Autotag is based on the last digits of the point names in the Point Collection. Please make sure that the point names end in the same number as the one visible on the Autotag. Other formats are also acceptable as long as the point ends in a number. For more information: Single point measurements / GCPs PIX4Dcatch
      Examples:
      • PointName_1 corresponds to Autotag 01
      • PointName_2 corresponds to Autotag 02
      Important: Currently, Pix4D supports 55 different Autotags and a maximum of 55 point suffixes can be mapped.

      Start capturing a project in which the Autotags are visible. Below the Signal Indicator dialog, the total number of successfully marked Autotags and the number of point entries in the Point Collection is displayed. If a Autotag is detected in the image, it will be marked with the corresponding name on white background. If the Autotag is detected in at least 3 images, the Autotag label will turn green with a black background. To use a Autotag/GCP for processing, it has to be detected in at least 3 images.

      IMG_0271 IMG_0272

      During capturing, at least 3 of the marked Autotags/GCPs need to be displayed with green labels. Once the project capturing is finished, a capture report will be displayed with the scanned scene and the marked Autotags.

      IMG_0275

      After capturing, the Point Collection and detected marks can be exported by clicking on Export Points & Marks. These detected marks replace the tedious manual marking and can be used as automatically marked GCPs or as MTPs for further processing in photogrammetry software.

      IMG_0274

      In PIX4Dmatic, the marks can be imported and used as MTPs to improve the 3D reconstruction (1st screenshot below). Alternatively, the GCPs and automatic marks can be imported to improve the positioning accuracy of the 3D reconstruction (2nd screenshot below). To process a project with Autotags, please visit Processing projects - PIX4Dcatch.

      Tip: For a more detailed review of the automatic Autotag detection, please open the Project; on the bottom part, select GCP_marking_mark_points.png Mark Points. The Autotags imported to the Point Collection, the list of images, and a map of the capturing path will be displayed. By selecting a Autotag and an image, the marking accuracy can be validated. The map helps to identify the images where the selected Autotag was detected. To add or remove markings, please follow the procedure described in the next section: Manual marking of Autotags and GCPs.

      Importing Marks in PIX4Dmatic

      Pix4DMatic_AutoTags_MTP_workflow.png

      Importing GCPs and Marks in PIX4Dmatic

      Pix4DMatic_AutoTags_GCP_workflow.png

      Manual Marking of GCPs and Autotags

      GCPs and Autotags can be marked manually in PIX4Dcatch to have fully automated processing in PIX4Dcloud.

      1. file_blk.jpg Open the Project management dialog.

        PIX4Dcatch_projects.jpg

      2. Select the project.

        GCP_marking_grey.png

      3. GCP_management.png Open the Point Collection selection dialog you want to use as GCPs.

        GCP_marking_import_collection.png

        Important: The Point Collection has to be measured in a projected coordinate reference system.
      4. Close the dialog; the GCP icon will turn blue and the selected Point Collection will be displayed. Now, the Mark points is also available.

        PIX4Dcatch_GCP_marking_blue.png

      5. GCP_marking_mark_points.png Select Mark points.
      6. Select the GCP from the list to mark.

        PIX4Dcatch_GCP_marking_selection_b.png

      7. Mark GCP on an image with a single tap. Hold tapping a bit longer to open the magnifier for more accurate marking. To expand an image or delete a marking, tap the 3 dots at the top right of each image.

        PIX4Dcatch_GCP_marking_simple_b.png PIX4Dcatch_GCP_marking_magnifier_b.png

      8. Mark the GCP in at least 2 images.
      9. GCPs will be shown on the map; marked images will be displayed in green on the map.
      10. Tap Save.
      11. Select and mark additional GCPs for the project as before.
      Tip: For maximum absolute accuracy, mark GCPs in as many images as possible and also use the maximum zoom level to be able to mark the GCP at the pixel size level.

      To process a project with GCPs, please visit Processing projects - PIX4Dcatch.

      Optimize Autotags

      To optimize the autotags in your PIX4Dcatch project, follow these steps:

      1. Once you have completed the project capture with Tag Detection feature active navigate to the project screen.
      2. Tap on the three dots (menu) icon located on the upper-right corner of the project interface.
        IMG_0984
      3. From the dropdown menu, select "Optimize Autotags".

        IMG_0985

      Optimize Autotags when saving

      In the Project Settings section of the application, you will find a setting to automatically set a setting to optimize the autotags when saving.

      In this setting you can configure the frequency of the optimization of the Autotags:

      • Always, ​​

      • Ask each time,

      • Skip

      Note: If your project was captured with Tag Detection feature active the app will prompt you a message to automatically optimize the Autotags while saving the project.

      This action will initiate the optimization process, where the application will refine and improve the detected autotags for higher accuracy in your project. A progress screen will appear, showing the ongoing optimization. Once the process is complete, your project will be optimized, ensuring better accuracy and reliability for further processing, especially when using ground control points (GCPs) and other geospatial data.