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 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.

[1]  John F. Lucas.  Design and Evaluation of 3D Multiple Object Selection Techniques , 2005 .

[2]  Yanmin Wang,et al.  Pickup of large scale point cloud based on GPU , 2014 .

[3]  M. Rothermel,et al.  AN OUT-OF-CORE OCTREE FOR MASSIVE POINT CLOUD PROCESSING , 2014 .

[4]  Marco Attene,et al.  Hierarchical Structure Recovery of Point‐Sampled Surfaces , 2010, Comput. Graph. Forum.

[5]  Tobias Isenberg,et al.  Efficient Structure-Aware Selection Techniques for 3D Point Cloud Visualizations with 2DOF Input , 2012, IEEE Transactions on Visualization and Computer Graphics.

[6]  Sven Oesau,et al.  Planar Shape Detection and Regularization in Tandem , 2016, Comput. Graph. Forum.

[7]  T. Rabbani,et al.  EFFICIENT HOUGH TRANSFORM FOR AUTOMATIC DETECTION OF CYLINDERS IN POINT CLOUDS , 2005 .

[8]  George Vosselman,et al.  Two algorithms for extracting building models from raw laser altimetry data , 1999 .

[9]  Michael Wand,et al.  Interactive Editing of Large Point Clouds , 2007, PBG@Eurographics.

[10]  Michael Wimmer,et al.  Eurographics Symposium on Point-based Graphics (2006) Instant Points: Fast Rendering of Unprocessed Point Clouds , 2022 .

[11]  Andreas Nüchter,et al.  One billion points in the cloud – an octree for efficient processing of 3D laser scans , 2013 .

[12]  Marc Stamminger,et al.  Sequential point trees , 2003, ACM Trans. Graph..

[13]  Reinhard Klein,et al.  Efficient RANSAC for Point‐Cloud Shape Detection , 2007, Comput. Graph. Forum.

[14]  陈宝权.  GlobFit: Consistently Fitting Primitives by Discovering Global Relations , 2011 .

[15]  Satoshi Matsuoka,et al.  Teddy: A Sketching Interface for 3D Freeform Design , 1999, SIGGRAPH Courses.

[16]  Enrico Gobbetti,et al.  Layered Point Clouds , 2004, PBG.

[17]  Robert F. Tobler.  The rkd-Tree : An Improved kd-Tree for Fast n-Closest Point Queries in Large Point Sets , 2011 .

[18]  Claus Scheiblauer,et al.  Potree : Rendering Large Point Clouds in Web , 2016 .

[19]  Herbert Freeman,et al.  Determining the minimum-area encasing rectangle for an arbitrary closed curve , 1975, CACM.

[20]  Marc Levoy,et al.  QSplat: a multiresolution point rendering system for large meshes , 2000, SIGGRAPH.

[21]  Claus Scheiblauer,et al.  Interactions with Gigantic Point Clouds , 2014 .

[22]  Roland Wahl,et al.  Ransac Based Out-of-Core Point-Cloud Shape Detection for City-Modeling , 2007 .

[23]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[24]  Radu Bogdan Rusu,et al.  3D is here: Point Cloud Library (PCL) , 2011, 2011 IEEE International Conference on Robotics and Automation.

[25]  Michael Wimmer,et al.  Out-of-core selection and editing of huge point clouds , 2011, Comput. Graph..

[26]  Tim Weyrich,et al.  Post-processing of Scanned 3D Surface Data , 2004, PBG.

[27]  William E. Lorensen,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.