Least-Squares Fitting of Two 3-D Point Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Surface reconstruction from unorganized points
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Surface recovery from range images using curvature and motion consistency
Computer Vision and Image Understanding
ICP Registration Using Invariant Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Angular Difference Function and Its Application to Image Registration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Alignment of Multi-view Range Data to CAD Model
SMI '06 Proceedings of the IEEE International Conference on Shape Modeling and Applications 2006
A review of recent range image registration methods with accuracy evaluation
Image and Vision Computing
SGP '05 Proceedings of the third Eurographics symposium on Geometry processing
Multi-scale features for approximate alignment of point-based surfaces
SGP '05 Proceedings of the third Eurographics symposium on Geometry processing
Multiview registration for large data sets
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
Normal estimation for point clouds: a comparison study for a Voronoi based method
SPBG'05 Proceedings of the Second Eurographics / IEEE VGTC conference on Point-Based Graphics
Image registration by normalized mapping
Neurocomputing
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This paper proposes an angular-invariant feature for 3-D registration procedure to perform reliable selection of point correspondence. The feature is a k-dimensional vector, and each element within the vector is an angle between the normal vector and one of its k nearest neighbors. The angular feature is invariant to scale and rotation transformation, and is applicable for the surface with small curvature. The feature improves the convergence and error without any assumptions about the initial transformation. Besides, no strict sampling strategy is required. Experiments illustrate that the proposed angular-based algorithm is more effective than iterative closest point (ICP) and the curvature-based algorithm.