Alignment of three-dimensional point clouds using combined descriptors
Image Processing Theory, Tools and Applications(2014)
摘要
This paper presents a new methodology for aligning three-dimensional (3D) models of objects, based on point correspondences. In this case, objects are modelled as 3D point clouds. The proposed methodology considers pairs of such point clouds and firstly down-samples them in order to further improve processing time. Then, corresponding points are allocated between the processed point clouds, by using a novel combinational descriptor scheme. Finally, a global transformation is estimated from the inliers of the obtained correspondences. This transformation is used to align the two point clouds. The proposed methodology was applied to five pairs of large scale 3D point clouds. Results indicate that the proposed scheme achieved satisfactory alignment accuracy for all tested data pairs.
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关键词
computational geometry,sampling methods,3D object model alignment,3D point cloud modelling,combinational descriptor scheme,global transformation estimation,inliers,point allocation,point cloud down-sampling,point cloud processing,point correspondences,processing time improvement,three-dimensional object model alignment,three-dimensional point cloud alignment,3D modelling,3D object registration,Point Correspondence,Point clouds,Surface descriptors
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