Nokia Technologies Oy

Qiang Wu

Lixin Fan

Jian Zhang

Yu You

Furong Peng

Xiaoshui Huang

A method comprising: providing a first 3D point cloud and a second 3D point cloud about an object obtained using different sensing techniques; removing a scale difference between the 3D point clouds based on a mean distance of points in corresponding subsets of the first and second 3D point clouds; arranging the 3D point clouds in a two-level structure, wherein a first level is a macro structure describing boundaries of the object and a second level is a micro structure consisting of supervoxels of the 3D point cloud; constructing a first graph from the first 3D point cloud and a second graph from the second 3D point cloud such that the supervoxels represent nodes of the graphs and adjacencies of the supervoxels represents edges of the graphs; matching the first and second graph for obtaining a transformation matrix; and registering the 3D point clouds together by applying the transformation matrix.

Point cloud matching method

Abstract A method comprising: providing a first three-dimensional (3D) point cloud obtained according to a first sensing technique, a second 3D point cloud obtained according to a second sensing technique and a first radius of a sphere covering a real object underlying the second first 3D point cloud and a second radius of a sphere covering a real object underlying the second 3D point cloud; defining scales of the first 3D point cloud and the second 3D point cloud based on said first radius and second radius; searching statistically substantially similar candidate regions between the first 3D point cloud and the second 3D point cloud using an ensemble shape function (ESF); aligning the statistically substantially similar candidate regions between the first 3D point cloud and the second 3D point cloud at least in vertical direction; and ranking the aligned candidate regions between the 3D point clouds based on their structural similarity.

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Ultimo, Australia

Qiang Wu