Remove Existing SmartPoints: Check this when rerunning SmartMatch to first remove all existing SmartPoints from the project.
Point Density: There are several density settings - Low, Medium (default), High, Very High, and Extra High. Higher settings can be used when your photos have a weak random texture in order to increase point counts, or with good strong random texture to get a reasonably dense point cloud that can be used for further processing. The benefit of the Low setting is mainly speed, but it can sometimes result in unoriented photos due to a lack of shared SmartPoints. In most cases, the Medium setting should produce a good result in a reasonable time.
Good Overlap Threshold: Photos are assessed at the beginning of SmartMatch to determine if there is suitable overlap and if there is, the photos are considered for feature detection and matching. Lowering this threshold will result in a higher number of pairs being considered for matching with the negative consequence of sometimes matching photos that should not be (resulting in bad orientations).
Match Quality Threshold: This ‘match tolerance’ threshold can be used to control the number of matched features. A lower value will limit the number of features, while a higher value will allow for more features. Feature matches are ranked and ordered, so allowing a higher number will allow through more relatively weak feature matches. Sometimes a higher number of features is needed to orient more photos but the number of features vs quality of matches is a trade-off. Very weak points can harm orientation, but if there are enough of them at an acceptable quality, they can improve orientation.
Match photos with manual reference: This setting allows ‘forced’ matching between pairs, even if the ‘Good Overlap Percent’ is not satisfied. If a pair of photos has at least one manually referenced point, the pair will be used during matching. This can help with projects with limited overlap (or differences in scale) when re-running SmartMatch due to remaining unoriented photos.
Use Masks: You can use established DSM Masks to block regions of an image so that no SmartMatch feature detection takes place in the masked region. This can be useful when modeling an object with a textured background, where SmartPoints are not wanted off the model object. See also Texture and DSM Mask image file names. Click the Create Masks... hyperlink/verb below the setting to open the Create Masks Dialog to generate mask files based on a photos alpha channel or Chroma-key. If you start a SmartMatch project before masks have been created and/or applied, close the SmartPoints project dialog before running SmartMatch. You can then open the Create Masks Dialog to generate mask files, or assign established mask files via the Photo Properties dialog. Once your mask files are ready and assigned to appropriate photos you can then run SmartMatch using the Run SmartMatch Project... button, or Project menu item, and enable masking on the SmartPoints Project Options Dialog.
Triangulate SmartPoints: Check this to run triangulation on the generated SmartPoints. A low density triangulation will not be as detailed as say an MVS triangulation, but it is quicker and may be adequate for some projects.