@InProceedings{nguyen:3ddim99,
  author =	 {V.\ -D. Nguyen and V.\ Nzomigni and C.\ V.\ Stewart},
  title =	 {Fast and robust registration of 3D surfaces using
                  low curvature patches},
  booktitle =	 {3-D Digital Imaging and Modeling,
                  1999. Proceedings. Second International Conference
                  on},
  year =	 1999,
  pages =	 {201--208},
  keywords =	 {computational geometry, image matching, image
                  registration, 3D surfaces, M-estimator,
                  Robust-Closest-Patch algorithm, approximate normal
                  distance, current pose, data surfaces, error
                  standard deviation, linear system, local surface
                  normal, low curvature patches, model patch matching,
                  noisy data, online registration, pose, range data,
                  range data registration algorithm, registration
                  techniques, rigid pose parameters, robust
                  registration, rotation constraint, symmetric
                  formulation, turbine blade inspection},
  abstract =	 {The paper describes a novel range data registration
                  algorithm, specifically designed for accuracy,
                  speed, and robustness. Like many recent registration
                  techniques, our Robust-Closest-Patch algorithm (RCP)
                  iteratively matches model patches to data surfaces
                  based on the current pose and then re-estimates pose
                  based on these matches. RCP has several novel
                  features: 1) online registration is driven by low
                  curvature patches computed from the model offline;
                  2) an approximate normal distance between a patch
                  and a surface is used, avoiding the need to estimate
                  local surface normal and curvature from noisy data;
                  3) pose is solved exactly by a linear system in six
                  parameters, using a symmetric formulation of the
                  rotation constraint; 4) robustness is ensured using
                  an M-estimator that estimates both the rigid pose
                  parameters and the error standard deviation. Results
                  are shown using models and range data from turbine
                  blade inspection},
  annote =	 {}
}