Beiträge in Tagungsbänden:

H. Steinlechner, B. Rainer, M. Schwärzler, G. Haaser, A. Szabo, S. Maierhofer, M. Wimmer:
"Adaptive Point-cloud Segmentation for Assisted Interactions";
in: "Proceedings of the 33rd Symposium on Interactive 3D Graphics and Games (I3D 2019)", 14; ACM (Hrg.); herausgegeben von: ACM; ACM Digital Library, 2019, ISBN: 978-1-4503-6310-5, S. 1 - 9.

Kurzfassung englisch:
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 interactions that utilize this local semantic information to improve the user´s workflow.

Pointcloud Segmentation, Shape Detection, Interactive Editing

"Offizielle" elektronische Version der Publikation (entsprechend ihrem Digital Object Identifier - DOI)

Elektronische Version der Publikation:

Erstellt aus der Publikationsdatenbank der Technischen Universität Wien.