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Initial Pose Estimation for 3D Models Using Learned Objective Functions (bibtex)
  author = {Wimmer, Matthias and Radig, Bernd},
  title = {Initial Pose Estimation for {3D} Models Using Learned Objective Functions},
  booktitle = {Proceedings of the 8th Asian Conference on Computer Vision ({ACCV07)}},
  year = {2007},
  editor = {Yagi, Yasushi and Kang, Sing Bing and Kweon, In So and Zha, Hongbin},
  volume = {4844},
  series = {{LNCS}},
  pages = {332--341},
  address = {Heidelberg},
  month = nov,
  publisher = {Springer},
  abstract = {Tracking {3D} models in image sequences essentially requires determining
	their initial position and orientation. Our previous work identifies
	the objective function as a crucial component for fitting {2D} models
	to images. We state preferable properties of these functions and
	we propose to learn such a function from annotated example images.
	This paper extends this approach by making it appropriate to also
	fit {3D} models to images. The correctly fitted model represents
	the initial pose for model tracking. However, this extension induces
	nontrivial challenges such as out-of-plane rotations and self occlusion,
	which cause large variation to the models surface visible in the
	image. We solve this issue by connecting the input features of the
	objective function directly to the model. Furthermore, sequentially
	executing objective functions specifically learned for different
	displacements from the correct positions yields highly accurate objective
  isbn = {978-3-540-76389-5}
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Last edited 29.01.2013 17:37 by Quirin Lohr