Adaptive Pointcloud Segmentation for Assisted Interactions

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 usercontrolled 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 interactions that utilize this local semantic information to improve the user’s workflow.

@InProceedings { SRSH19,
  author       = "Steinlechner, Harald and Rainer, Bernhard and Schw{\"a}rzler, Michael and Haaser, Georg",
  title        = "Adaptive Pointcloud Segmentation for Assisted Interactions",
  booktitle    = "Symposium on Interactive 3D Graphics and Games (I3D 2019), May 21-23, 2019, Montreal, QC, Canada,
                  https://doi.org/10.1145/3306131.3317023.",
  year         = "2019",
  publisher    = "ACM Press",
  url          = "http://graphics.tudelft.nl/Publications-new/2019/SRSH19"
}