IS = { zkontrolovano 15 Jan 2013 },
  UPDATE  = { 2012-10-01 },
author =      {Pet{\v r}{\'\i}{\v c}ek, Tom{\'a}{\v s}},
supervisor =  {Svoboda, Tom{\'a}{\v s}},
title =       {3D Object Recognition and Pose Estimation -- {PhD} Thesis Proposal},
institution = {Center for Machine Perception, K13133 FEE
               Czech Technical University},
address =     {Prague, Czech Republic},
year =        {2012},
month =       {August},
type =        {Research Report},
number =      {CTU--CMP--2012--19},
issn =        {1213-2365},
pages =       {30},
figures =     {5},
authorship =  {100},
psurl       = {[Petricek-TR-2012-19.pdf]},
project =     {GACR P103/10/1585, FP7-ICT-247870 NIFTi},
annote =      {The thesis proposal deals with 3D object recognition
    and pose estimation in presence of occlusion and clutter. The task
    has major applications in robotics, including automatic object
    manipulation or autonomous mobile robots in urban search and
    rescue. Methods based on matching local invariant features have
    been the most successful in solving the task and currently
    constitute the state of the art. We identify separate subtasks
    actually being solved with feature-based methods and briefly
    survey state-of-the-art methods from the perspective of these
    subtasks. Our method dealing with the problem of non-uniform
    sampling density is presented and evaluated on a standard publicly
    available dataset. The main open problems are identified to serve
    as a basis for possible research directions.},
keywords =    {3D object recognition, pose estimation},