Point cloud matching method

An apparatus comprising at least one processor, memory including computer program code, the memory and the computer program code configured to, with the at least one processor, cause the apparatus to at least:

• provide a first three-dimensional (3D) point cloud obtained according to a first sensing technique about an object underlying the first 3D point cloud, and a second 3D point cloud obtained according to a second sensing technique about said object;
• remove a scale difference between the first 3D point cloud and the second 3D point cloud based on a mean distance of points in a subset of the first 3D point cloud and a corresponding subset of the second 3D point cloud;
• arrange the first 3D point and the second 3D point cloud in a two-level structure, wherein a first level is a macro structure describing boundaries of said object and a second level is a micro structure consisting of supervoxels comprising points of 3D point cloud having similar properties clustered together;
• construct 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 first graph and the second graph and adjacent relations of the supervoxels represents edges of the first graph and the second graph;
• carry out a graph matching process between the first graph and the second graph for obtaining a transformation matrix;
• and
• register the first 3D point cloud and the second 3D point clouds substantially together by applying the transformation matrix.

A non-transitory computer readable medium comprising program instructions for causing an apparatus to perform at least the follow:

• providing a first three-dimensional (3D) point cloud obtained according to a first sensing technique about an object underlying the first 3D point cloud, and a second 3D point cloud obtained according to a second sensing technique about said object;
• removing a scale difference between the first 3D cloud and the second 3D point cloud based on a mean distance of points in a subset of the first 3D point cloud and a corresponding subset of the second 3D point cloud;
• arranging the first 3D cloud and the second 3D point cloud in a two-level structure, wherein a first level is a macro structure describing boundaries of said object and a second level is a micro structure consisting of supervoxels comprising points of 3D point cloud having similar properties clustered together;
• 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 first graph and the second graph and adjacent relations of the supervoxels represents edges of the first graph and the second graph;
• carrying out a graph matching process between the first graph and the second graph for obtaining a transformation matrix;
• and
• registering the first 3D cloud and the second 3D point clouds substantially together by applying the transformation matrix.