Hello, I have raster and vector data represented as point clouds in 2d. Raster data extracted as contours so it contain some noise. Raster and vector data slightly differs in scale and position,raster data can also have some small perspective distortion and barrel distortion, but I can provide initian alignment. I want to raster data "converged" to unchangeble vector data, so I think I want some kind of non-rigid point cloud matching. By alignment of 2 point clouds then I want to determine some parameters(maybe TPS spline?) and unwarp raster image and then draw vector image above it.

For example if we use homography matrix and assume perspective transform we have 8 parameters. I tried to use KCReg it use some specific metric for point clouds, but it give me unsatisfactory results because my distortion can be only roughly expressed by perspective transform and I need some more non-rigid alignment. So the task consist of some function E(p)(metric)+minimizer+bounds for minimizer. I also tried gmmerg. I seems it uses some Broyden Fletcher Goldfarb Shannon minimization from VNL, but I managed to achive good results only for simple tasks.