Adaptive pointcloud segmentation for assisted interactions

Conference paper (2019)

Authors

Harald Steinlechner VRVis Research Center
Bernhard Rainer AIT Austrian Institute of Technology
M. Schwarzler Computer Graphics and Visualisation - EEMCS
Georg Haaser VRVis Research Center
Attila Szabó VRVis Research Center
Stefan Maierhofer VRVis Research Center
Michael Wimmer Technische Universität Wien
Research Group
Computer Graphics and Visualisation (EEMCS) (TU Delft)
Copyright: © 2019 Harald Steinlechner, Bernhard Rainer, M. Schwarzler, Georg Haaser, Attila Szabo, Stefan Maierhofer, Michael Wimmer
DOI: https://doi.org/10.1145/3306131.3317023
Interactive Editing Pointcloud Segmentation Shape Detection
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Published Date

21-05-2019

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Computer Graphics and Visualisation

Abstract

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.