![](bg7.png)
Belongie, S., Malik, J., and Puzicha, J. (2002). Shape
matching and object recognition using shape con-
texts. Pattern Analysis and Machine Intelligence,
IEEE Transactions on, 24(4):509 –522.
Drost, B., Ulrich, M., Navab, N., and Ilic, S. (2010). Model
globally, match locally: Efficient and robust 3D object
recognition. In Computer Vision and Pattern Recog-
nition (CVPR), 2010 IEEE Conference on, pages 998
–1005.
Fischler, M. A. and Bolles, R. C. (1981). Random sample
consensus: a paradigm for model fitting with appli-
cations to image analysis and automated cartography.
Commun. ACM, 24(6):381–395.
Frome, A., Huber, D., Kolluri, R., Bulow, T., and Malik,
J. (2004). Recognizing objects in range data using
regional point descriptors. In Proceedings of the Eu-
ropean Conference on Computer Vision (ECCV).
Hetzel, G., Leibe, B., Levi, P., and Schiele, B. (2001). 3D
object recognition from range images using local fea-
ture histograms. In Proceedings of the 2001 IEEE
Computer Society Conference on Computer Vision
and Pattern Recognition (CVPR)., volume 2, pages
394–399.
Jensen, L., Kjær-Nielsen, A., Pauwels, K., Jessen, J.,
Van Hulle, M., and Krger, N. (2010). A two-level
real-time vision machine combining coarse- and fine-
grained parallelism. Journal of Real-Time Image Pro-
cessing, 5:291–304. 10.1007/s11554-010-0159-4.
Johnson, A. and Hebert, M. (1999). Using spin im-
ages for efficient object recognition in cluttered 3D
scenes. Pattern Analysis and Machine Intelligence,
IEEE Transactions on, 21(5):433 –449.
Kr
¨
uger, N., Felsberg, M., and W
¨
org
¨
otter, F. (2004).
Processing multi-modal primitives from image se-
quences. Fourth International ICSC Symposium on
Engineering of Intelligent Systems.
Kuhn, H. W. (1955). The hungarian method for the assign-
ment problem. Naval Research Logistics Quarterly,
2(1-2):83–97.
Lowe, D. (1999). Object recognition from local scale-
invariant features. In Computer Vision, 1999. The Pro-
ceedings of the Seventh IEEE International Confer-
ence on, volume 2, pages 1150 –1157 vol.2.
Novatnack, J. and Nishino, K. (2008). Scale-
dependent/invariant local 3D shape descriptors
for fully automatic registration of multiple sets of
range images. In Proceedings of the 10th European
Conference on Computer Vision: Part III, ECCV ’08,
pages 440–453, Berlin, Heidelberg. Springer-Verlag.
Papazov, C. and Burschka, D. (2010). An efficient ransac
for 3D object recognition in noisy and occluded
scenes. In Proceedings of the 10th Asian Conference
on Computer Vision, pages 135–148. Springer-Verlag.
Payet, N. and Todorovic, S. (2011). From contours to 3D
object detection and pose estimation. In Computer Vi-
sion (ICCV), 2011 IEEE International Conference on,
pages 983 –990.
Pugeault, N., W
¨
org
¨
otter, F., and Kr
¨
uger, N. (2010). Visual
primitives: Local, condensed, and semantically rich
visual descriptors and their applications in robotics.
International Journal of Humanoid Robotics (Special
Issue on Cognitive Humanoid Vision), 7(3):379–405.
Rusu, R. B., Blodow, N., and Beetz, M. (2009). Fast point
feature histograms (FPFH) for 3D registration. In
Robotics and Automation, 2009. ICRA ’09. IEEE In-
ternational Conference on, pages 3212 –3217.
Stein, F. and Medioni, G. (1992). Structural indexing:
Efficient 3-D object recognition. Pattern Analy-
sis and Machine Intelligence, IEEE Transactions on,
14(2):125 –145.
Umeyama, S. (1991). Least-squares estimation of transfor-
mation parameters between two point patterns. Pat-
tern Analysis and Machine Intelligence, IEEE Trans-
actions on, 13(4):376 –380.
Wahl, E., Hillenbrand, U., and Hirzinger, G. (2003). Surflet-
pair-relation histograms: a statistical 3D-shape repre-
sentation for rapid classification. In 3-D Digital Imag-
ing and Modeling, 2003. 3DIM 2003. Proceedings.
Fourth International Conference on, pages 474 –481.
PoseEstimationusingaHierarchical3DRepresentationofContoursandSurfaces
111