We will now use the method outlined above to filter out the lowest confidence interval (in this case, set Min:0 and Max:5). The dialog that pops up allows you to set minimal and maximal confidences.įor example, try setting Min:50 and Max:255 to only show the Dense Cloud points wth the highest confidence.Īfter looking at the difference, reset the filter by clicking on Reset filter within the Tools/Dense Cloud menu. Proceed to Tools/Dense Cloud in the menu and click on Filter by confidence…. Luckily, Metashape allows selection and deletion of points by Point confidence. However, not all of them are scientifically repeatable - because let’s be honest, would you be able to exactly tell which of the points you removed from the data? No. There are various ways of cutting off parts of your Dense Cloud. To do the latter, right click on the Workspace/Dense Cloud (… points) and select Duplicate….įollowing alterations to the duplicated data set, you can always switch back to the original by right clicking Workspace/Dense Cloud (… points) and Set As Default. This can be done by either backing up the entire project ( Save as…) or by making a local copy of the data within the project. Make sure to always either backup your data before playing with it. If not, one can select this by clicking the four-dotted icon in the menu.

This may take a while, but assuming there is sufficient overlap between the data, a sparse point cloud will be shown on the screen (in the Model tab) once processing is done. Keep Generic preselection and Exclude stationary tie points selected.Īfter clicking OK, Metashape starts aligning your photos. Most important here is the Accuracy parameter, which governs whether your photos are down-sampled before alignment.įor now we’ll skip the other parameters just make sure to deselect Reference preselection, Guided image matching, and Adaptive camera model fitting.

Select Align Photos… in the Workflow menu.Ī dialog will pop up with several parameter options. In Metashape this first requires the estimation of camera positions for each photo, which are then used to build a sparse cloud. This process goes through all images in the project and tries to identify common features. With the photos now imported into Metashape and analysed, we can proceed with the alignment process. The standardised project structure (as we will see later on) is also used for automated processing.

Sfm model vmaker