Large scale cut plane: an occlusion management technique for immersive dense 3D reconstructions

Dense 3D reconstructions of real-world environments become wide spread and are foreseen to act as data base to solve real world problems, such as remote inspections. Therefore not only scene viewing is required but also the ability to interact with the environment, such as selection of a user-defined part of the reconstruction for later usage. However, inter-object occlusion is inherent to large dense 3D reconstructions, due to scene geometry or reconstruction artifacts that might result in object containment. Since prior art lacks approaches for occlusion management in environments that consist of one or multiple (large) continuous surfaces, we propose the novel technique Large Scale Cut Plane that enables segmentation and subsequent selection of visible, partly or fully occluded patches within a large 3D reconstruction, even at far distance. We combine Large Scale Cut Plane with an immersive virtual reality setup to foster 3D scene understanding and natural user interactions. We furthermore present results from a user study where we investigate performance and usability of our proposed technique compared to a baseline technique. Our results indicate Large Scale Cut Plane to be superior in terms of speed and precision, while we found need of improvement of the user interface. The presented investigations has to the authors' best knowledge not been subject to previous research.

[1]  Ken Hinckley,et al.  Passive real-world interface props for neurosurgical visualization , 1994, CHI '94.

[2]  Babak Taati,et al.  Difference of Normals as a Multi-scale Operator in Unorganized Point Clouds , 2012, 2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization & Transmission.

[3]  Ligang Liu,et al.  iCutter: a direct cut‐out tool for 3D shapes , 2011, Comput. Animat. Virtual Worlds.

[4]  Anh Nguyen,et al.  3D point cloud segmentation: A survey , 2013, 2013 6th IEEE Conference on Robotics, Automation and Mechatronics (RAM).

[5]  Dominique Bechmann,et al.  Starfish: a selection technique for dense virtual environments , 2012, VRST '12.

[6]  Karin Coninx,et al.  Exploring the Effects of Environment Density and Target Visibility on Object Selection in 3D Virtual Environments , 2007, 2007 IEEE Symposium on 3D User Interfaces.

[7]  Youyi Zheng,et al.  Mesh Decomposition with Cross‐Boundary Brushes , 2010, Comput. Graph. Forum.

[8]  Daniel Alejandro Winkler Rosa,et al.  Selection Techniques for Dense and Occluded Virtual 3D Environments, Supported by Depth Feedback: Double, Bound and Depth Bubble Cursors , 2010, 2010 XXIX International Conference of the Chilean Computer Science Society.

[9]  Chengcui Zhang,et al.  A progressive morphological filter for removing nonground measurements from airborne LIDAR data , 2003, IEEE Trans. Geosci. Remote. Sens..

[10]  Ferran Argelaguet,et al.  A survey of 3D object selection techniques for virtual environments , 2013, Comput. Graph..

[11]  Michael Wimmer,et al.  Seamless texturing of archaeological data , 2013, 2013 Digital Heritage International Congress (DigitalHeritage).

[12]  Ivan Poupyrev,et al.  The go-go interaction technique: non-linear mapping for direct manipulation in VR , 1996, UIST '96.

[13]  Joseph J. LaViola,et al.  Dense and Dynamic 3D Selection for Game-Based Virtual Environments , 2012, IEEE Transactions on Visualization and Computer Graphics.

[14]  Mark Green,et al.  JDCAD: A highly interactive 3D modeling system , 1994, Comput. Graph..

[15]  Hannes Kaufmann,et al.  DrillSample: precise selection in dense handheld augmented reality environments , 2013, VRIC.

[16]  Aaron Hertzmann,et al.  Learning 3D mesh segmentation and labeling , 2010, ACM Trans. Graph..

[17]  Pere-Pau Vázquez,et al.  Way‐Finder: guided tours through complex walkthrough models , 2004, Comput. Graph. Forum.

[18]  Ligang Liu,et al.  Easy Mesh Cutting , 2006, Comput. Graph. Forum.

[19]  Frank Steinicke,et al.  Touching the Cloud: Bimanual annotation of immersive point clouds , 2014, 3DUI.

[20]  Federico Tombari,et al.  Real-time and scalable incremental segmentation on dense SLAM , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[21]  J. B. Brooke,et al.  SUS: A 'Quick and Dirty' Usability Scale , 1996 .

[22]  Niklas Elmqvist,et al.  A Taxonomy of 3D Occlusion Management for Visualization , 2008, IEEE Transactions on Visualization and Computer Graphics.

[23]  Steven K. Feiner,et al.  The Flexible Pointer: An Interaction Technique for Selection in Augmented and Virtual Reality , 2003 .

[24]  Matthias Nießner,et al.  Real-time 3D reconstruction at scale using voxel hashing , 2013, ACM Trans. Graph..

[25]  Lubin Fan,et al.  Paint Mesh Cutting , 2011, Comput. Graph. Forum.

[26]  Michela Bertolotto,et al.  Octree-based region growing for point cloud segmentation , 2015 .

[27]  Marsette Vona,et al.  Moving Volume KinectFusion , 2012, BMVC.