In this work, we propose an interaction-driven approach streamlined to support and improve a wide range of real-time 2D interaction metaphors for arbitrarily large pointclouds based on detected primitive shapes. Rather than performing shape detection as a costly pre-processing step on the entire point cloud at once, a user-controlled interaction determines the region that is to be segmented next. By keeping the size of the region and the number of points small, the algorithm produces meaningful results and therefore feedback on the local geometry within a fraction of a second. We can apply these finding for improved picking and selection metaphors in large point clouds, and propose further novel shape-assisted.

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CT data of industrially produced cast metal parts are often afflicted with artefacts due to complex geometries ill-suited for the scanning process. Simple global threshold-based porosity detection algorithms usually fail to deliver meaningful results. Other adaptive methods can handle image artefacts, but require long preprocessing times. This makes an efficient analysis workflow infeasible. We propose an alternative approach for analyzing and visualizing volume defects in a fully interactive manner, where analyzing volumes becomes more of an interactive exploration instead of time-consuming parameter guessing interrupted by long processing times. Our system is based on a highly efficient GPU implementation of a segmentation algorithm for porosity detection. The runtime is on the order of seconds for a full volume and parametrization is kept simple due to a single threshold parameter. A fully interactive user interface comprised of multiple linked views allows to quickly identify defects of interest, while filtering out artefacts even in noisy areas.@en