Deep learning on 3D data is still challenging — partly due to the computational complexity of training on 3D grids. Thus, alternative data structures become more and more interesting. PointNets, for example, operate directly on unordered set of points, i.e. point clouds. Similarly, Point Set Generation Networks are able to directly predict point clouds from images. In this article, I briefly summarize both ideas.
This article presents visualizations of the qualitative and quantitative results from the superpixel benchmark published in CVIU. Based on NVD3 and Unite Gallery, the visualizations allow to interactively compare different superpixel algorithms in the browser.