Abstract: Some embodiments are directed to a computer implemented method for simplification of a point cloud including a set of points. The method comprises implementing recursive spatial partitioning of the set of points into a hierarchy of clusters, identifying representative points within each cluster in the hierarchy and, for each representative point, defining a point-pair that consists of or includes the representative point and a representative point of an immediate parent cluster. The method further includes calculating a contraction error metric for each point-pair, and iteratively contracting the point-pair with the lowest contraction error metric, updating remaining point-pairs as a result of the contraction, and revising the contraction error metric of the updated point-pairs accordingly.

Abstract: Some embodiments are directed to a computer implemented method for simplification of a point cloud including a set of points. The method comprises implementing recursive spatial partitioning of the set of points into a hierarchy of clusters, identifying representative points within each cluster in the hierarchy and, for each representative point, defining a point-pair that consists of or includes the representative point and a representative point of an immediate parent cluster. The method further includes calculating a contraction error metric for each point-pair, and iteratively contracting the point-pair with the lowest contraction error metric, updating remaining point-pairs as a result of the contraction, and revising the contraction error metric of the updated point-pairs accordingly.

Abstract: Some embodiments are directed to a method of cloud point simplification that includes implementing recursive spatial partitioning of the set of points into a hierarchy of clusters and, for each cluster, calculating a tangent plane estimate for the points in the cluster and deriving a confidence factor for the calculated tangent plane estimate. The method further includes identifying representative points within each cluster in the hierarchy and, for each representative point, defining a point-pair that consists of the representative point and a representative point of an immediate parent cluster; calculating a contraction error metric for each point-pair that is weighted by the inverse of the confidence factors calculated for the clusters associated with each representative point; and iteratively contracting the point-pair with the lowest contraction error metric, updating remaining point-pairs as a result of the contraction, and revising the contraction error metric of the updated point-pairs accordingly.

Abstract: A computer implemented method of processing elevation mesh data defining a polygon mesh surface to determine an alternative representation of the surface using fewer polygons than in the original representation is disclosed. Initially original elevation mesh data is sub-sampled to generate a low resolution representation of the polygon mesh surface where vertices of polygons correspond to points on a coarser X-Y grid than the original elevation mesh data. Error values indicative of the extent that portions of the surface defined by the low resolution elevation mesh data differ from corresponding portions of the surface defined by the original elevation mesh data are then determined.

Abstract: Spatial errors identifying differences between spatial co-ordinates of vertices of a mesh surface defined by mesh data and corresponding vertices in a mesh surface generated from an encoded representation of the mesh data are determined. Mesh data and texture co-ordinate data associating vertices of a mesh surface with locations in an image space of an image to be rendered onto the mesh surface are then processed to determine mapping between the co-ordinate space of the mesh data and the image space of an image to be rendered onto the mesh surface. Texture co-ordinate data for each vertex is then modified to associate a vertex with a location in image space which differs from the location identified by the unmodified texture co-ordinate data by an amount corresponding to the projection the spatial errors associated with the vertex to the image space.

Abstract: Spatial errors identifying differences between spatial co-ordinates of vertices of a mesh surface defined by mesh data and corresponding vertices in a mesh surface generated from an encoded representation of the mesh data are determined. Mesh data and texture co-ordinate data associating vertices of a mesh surface with locations in an image space of an image to be rendered onto the mesh surface are then processed to determine mapping between the co-ordinate space of the mesh data and the image space of an image to be rendered onto the mesh surface. Texture co-ordinate data for each vertex is then modified to associate a vertex with a location in image space which differs from the location identified by the unmodified texture co-ordinate data by an amount corresponding to the projection the spatial errors associated with the vertex to the image space.

Abstract: A method of compressing data defining a colored polygonal mesh surface is disclosed. Color data associating points on a polygonal mesh surface is processed to determine a set of representative colors for coloring the polygonal mesh surface. The mesh surface is then divided into a number of sub-meshes each associated with one of the representative set of colors, wherein each of the sub-meshes comprises the portions of the colored polygonal mesh surface associated with colors which are determined to be similar to the representative color associated with the sub-mesh. An alternative representation of the colored polygonal mesh surface is then generated comprising mesh data defining geometry and connectivity of polygonal mesh surfaces corresponding to each of the sub-meshes and color data indicative the representative colors associated with the sub-meshes.