@COMMENT This file was generated by bib2html.pl <https://sourceforge.net/projects/bib2html/> version 0.94
@COMMENT written by Patrick Riley <http://sourceforge.net/users/patstg/>
@InProceedings{IEEE3DUI06-fbp, 
  author =      {Greg Schmidt and Dennis G. Brown and Erik B. Tomlin and J. Edward {Swan~II} 
                 and Yohan Baillot}, 
  title =       {Toward Disambiguating Multiple Selections for Frustum-Based Pointing}, 
  booktitle =   {Technical Papers, Proceedings of the 1st IEEE Symposium on 
                 3D User Interfaces (3DUI 2006)}, 
  year =        2006, 
  location =    {Alexandria, Virginia, USA}, 
  date =        {March 25--26}, 
  month =       {March}, 
  pages =       {87--94}, 
  abstract =    { 
Selection is a fundamental user operation in 3D environments. These environments 
often simulate or augment the real world, and a part of that simulation is the 
ability to select objects for observation and manipulation. Many user interfaces 
for these applications depend on six-degree-of-freedom tracking devices. Such 
devices have limited accuracy and are susceptible to noise, giving an 
imprecision that makes object selections difficult and hard to repeat.  This 
difficulty is amplified when the user's viewpoint is also tracked, meaning the 
user must compensate for noise from both the head tracker and the pointing 
device when performing object selection. Also, users may experience fatigue when 
using handheld pointing devices for extended periods, creating error even if the 
tracking technology were perfect. 
This paper presents a pointing-based probabilistic selection algorithm that 
addresses some of the ambiguities associated with tracking and user imprecision. 
It performs multiple selections by considering a frustum along the user's 
pointing direction and the hierarchical structure of the database. It assigns 
probabilities that the user has selected particular objects using a set of 
low-level 3D intersection-based selection techniques and the relationship of the 
objects in a hierarchical database, and makes the final selection using one of 
several weighting schemes.  We performed several experiments to evaluate the 
low-level selection techniques, tested several weighting schemes for the integration 
algorithm, and we show that the algorithm is effective at disambiguating multiple 
selections. 
}, 
}