Point cloud registration

A computer program stored in a non-transitory computer-readable medium which, when run on a computer, causes the computer to carry out a method comprising:

• determining a 2D overlapping portion between a source 2D projection of a source 3D point cloud and a target 2D projection of a target 3D point cloud, the source and target 3D point clouds representative of source and target geographical areas, respectively, the 2D overlapping portion corresponding to an overlapping region between the source and target geographical areas;
• dividing the 2D overlapping portion into a plurality of cells and determining at least one overlap cell which includes at least a first threshold number of points from the source 2D projection and at least a second threshold number of points from the target 2D projection;
• performing a comparison process for said overlap cell, the comparison process comprising:
• determining a plurality of target sets of points comprising a first target set of points of the target 3D point cloud falling within said overlap cell and second and further target sets of points of the target 3D point cloud falling within cells neighboring said overlap cell, respectively,
• determining a source set of points comprising points of the 3D source point cloud that fall within said overlap cell,
• comparing the source set with each of the plurality of target sets to determine a plurality of similarity scores for the plurality of target sets, respectively, and
• based on the similarity scores, selecting a best matching target set;
• determining a transformation between the points of the source set and the points of the best matching target set or vice versa;
• and
• applying the transformation to register the source 3D point cloud to the target 3D point cloud or vice versa.

An information processing apparatus comprising a memory and a processor connected to the memory, wherein the processor is configured to:

• determine a 2D overlapping portion between a source 2D projection of a source 3D point cloud and a target 2D projection of a target 3D point cloud, the source and target 3D point clouds representative of source and target geographical areas, respectively, the 2D overlapping portion corresponding to an overlapping region between the source and target geographical areas;
• divide the 2D overlapping portion into a plurality of cells and determine at least one overlap cell which includes at least a first threshold number of points from the source 2D projection and at least a second threshold number of points from the target 2D projection;
• perform a comparison process for said overlap cell, the comparison process comprising:
• determining a plurality of target sets of points comprising a first target set of points of the target 3D point cloud falling within said overlap cell and second and further target sets of points of the target 3D point cloud falling within cells neighboring said overlap cell, respectively,
• determining a source set of points comprising points of the 3D source point cloud that fall within said overlap cell,
• comparing the source set with each of the plurality of target sets to determine a plurality of similarity scores for the plurality of target sets, respectively, and
• based on the similarity scores, selecting a best matching target set;
• determine a transformation between the points of the source set and the points of the best matching target set or vice versa;
• and
• apply the transformation to register the source 3D point cloud to the target 3D point cloud or vice versa.

A computer-implemented method comprising:

• estimating a 3D overlap volume between a source 3D point cloud and a target 3D point cloud based on an overlapping region between source and target geographical areas, the source and target 3D point clouds representative of the source and target geographical areas, respectively;
• dividing the 3D overlap volume into a plurality of cells and determining at least one overlap cell which includes at least a first threshold number of points from the source 3D point cloud and at least a second threshold number of points from the target 3D point cloud;
• performing a comparison process for said overlap cell, the comparison process comprising:
• determining a plurality of target sets of points comprising a first target set of points of the target 3D point cloud which are in said overlap cell and second and further target sets of points of the target 3D point cloud which are in cells neighboring said overlap cell, respectively,
• determining a source set of points comprising points of the 3D source point cloud which are in said overlap cell,
• comparing the source set with each of the plurality of target sets to determine a plurality of similarity scores for the plurality of target sets, respectively, and
• based on the similarity scores, selecting a best matching target set;
• determining a transformation between the points of the source set and the points of the best matching target set or vice versa;
• and
• applying the transformation to register the source 3D point cloud to the target 3D point cloud or vice versa.

A computer program stored in a non-transitory computer-readable medium which, when run on a computer, causes the computer to carry out a method comprising:

• estimating a 3D overlap volume between a source 3D point cloud and a target 3D point cloud based on an overlapping region between source and target geographical areas, the source and target 3D point clouds representative of the source and target geographical areas, respectively;
• dividing the 3D overlap volume into a plurality of cells and determining at least one overlap cell which includes at least a first threshold number of points from the source 3D point cloud and at least a second threshold number of points from the target 3D point cloud;
• performing a comparison process for said overlap cell, the comparison process comprising:
• determining a plurality of target sets of points comprising a first target set of points of the target 3D point cloud which are in said overlap cell and second and further target sets of points of the target 3D point cloud which are in cells neighboring said overlap cell, respectively,
• determining a source set of points comprising points of the 3D source point cloud which are in said overlap cell,
• comparing the source set with each of the plurality of target sets to determine a plurality of similarity scores for the plurality of target sets, respectively, and
• based on the similarity scores, selecting a best matching target set;
• determining a transformation between the points of the source set and the points of the best matching target set or vice versa;
• and
• applying the transformation to register the source 3D point cloud to the target 3D point cloud or vice versa.

An information processing apparatus comprising a memory and a processor connected to the memory, wherein the processor is configured to:

• estimate a 3D overlap volume between a source 3D point cloud and a target 3D point cloud based on an overlapping region between source and target geographical areas, the source and target 3D point clouds representative of the source and target geographical areas, respectively;
• divide the 3D overlap volume into a plurality of cells and determine at least one overlap cell which includes at least a first threshold number of points from the source 3D point cloud and at least a second threshold number of points from the target 3D point cloud;
• perform a comparison process for said overlap cell, the comparison process comprising:
• determining a plurality of target sets of points comprising a first target set of points of the target 3D point cloud which are in said overlap cell and second and further target sets of points of the target 3D point cloud which are in cells neighboring said overlap cell, respectively,
• determining a source set of points comprising points of the 3D source point cloud which are in said overlap cell,
• comparing the source set with each of the plurality of target sets to determine a plurality of similarity scores for the plurality of target sets, respectively, and
• based on the similarity scores, selecting a best matching target set;
• determine a transformation between the points of the source set and the points of the best matching target set or vice versa;
• and
• apply the transformation to register the source 3D point cloud to the target 3D point cloud or vice versa.