Rationale for Vector of CartesianIndex{2} for output 'matches' of match_keypoints

I was wondering if someone could explain the design choice for making the match_keypoints function return Vector{CartesianIndex{2}}.

Coming from MATLAB, I was expecting this function to return two matrices, either an N x 2 matrix, or a 2 x N matrix, where the first matrix contains the points in the first view, and the second matrix contains the points in the second view. I work in the field of Multiple View Geometry (MVG), and am in the process of writing a package for Julia that achieves feature parity with MATLAB's Computer Vision toolbox. Typical steps in MVG include converting between homogeneous coordinates (i.e. appending an extra dimension with the value of '1'), standardising the homogeneous coordinates (dividing each point by its third dimension) and converting back to Cartesian coordinates (removing the third coordinate after standardising). Furthermore, we often need to transform coordinates by making the data points have zero mean, and scaling each data point to lie inside a unit box. These steps are very easy when the points are represented as matrices. For example, it allows one to write code such as mean(pts[1,:]) to obtain the mean x coordinate. Transforming all of the data points is also simple, since it just involves multiplying the points by a matrix.

I am struggling to perform these types of operations elegantly with the Vector{CartesianIndex{2}} data structure. At present, I intend to loop over this data structure and explicitly form the two matrices I mentioned. However, this duplication of work and unnecessary performance hit feels like a hack.

Perhaps we could write a second variant of the match_keypoints function that returns two matrices instead? Or alternatively, there is some elegant Julian way of achieving the transformations I mentioned with this Vector of CartesianIndex type?

Start at the match_keypoints entry point and inspect how its matches result is represented and consumed. Review the discussion about Vector{CartesianIndex{2}} versus matrix-based outputs; done means reaching and documenting a settled design decision or implementing the agreed return form.