In this work, we propose a new dimensionality reduction approach for generating low-dimensional embeddings of high-dimensional data based on an iterative procedure. The data set's dimensions are sorted depending on their variance. Starting with the highest variance, the dimensions are iteratively projected onto the embedding. The projection can be seen as taking a photo from a two-dimensional motive employing a depth effect. The approach is flexible and offers numerous extensions for future work. We introduce a basic variant and illustrate it working mechanisms with numerous visualizations. The approach is experimentally analyzed on a small set of benchmark problems. Exemplary embeddings and evaluations based on the Shepard-Kruskal measure and the co-ranking matrix complement the analysis. The new approach shows competitive results in comparison to well-established dimensionality reduction methods.