GPU-accelerated 2D point cloud visualization using smooth splines for visual analytics applications

We develop an efficient point cloud visualization framework. For efficient navigation in the visualization, we introduce a spline-based technique for the smooth approximation of discrete distance field data. Implemented on the GPU, the approximation technique allows for efficient visualizations and smooth zooming in and out of the distance field data. Combined with a template set of predefined, automatically or interactively adjustable transfer functions, the smooth distance field representation allows for an effective visualization of point cloud data at random abstraction levels. Using the presented technique, sets of point clouds can be effectively analyzed for intra- and inter-point cloud distribution characteristics. The effectiveness and usefulness of our approach is demonstrated by application on various point cloud visualization problems.