The representation, recognition, and locating of 3-d objects
International Journal of Robotics Research
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Object modelling by registration of multiple range images
Image and Vision Computing - Special issue: range image understanding
Robust methods for estimating pose and a sensitivity analysis
CVGIP: Image Understanding
Iterative point matching for registration of free-form curves and surfaces
International Journal of Computer Vision
Building 3-D models from unregistered range images
Graphical Models and Image Processing
A robust method for registration and segmentation of multiple range images
Computer Vision and Image Understanding
Towards a General Multi-View Registration Technique
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational Geometry for Design and Manufacture
Computational Geometry for Design and Manufacture
A survey of methods for recovering quadrics in triangle meshes
ACM Computing Surveys (CSUR)
Registration of combined range-intensity scans: Initialization through verification
Computer Vision and Image Understanding
Robustly registering range images using local distribution of albedo
Computer Vision and Image Understanding
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This paper describes a new model to 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) on-line registration is driven by low curvature patches computed from the model off-line; 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.