Point Cloud Filter Processing Using Representative Values

The patent divides the point cloud data into multiple local regions (e.g., using octree structure or grid division). For each local region, only the representative value (e.g., average position or color) is calculated and used for filtering, rather than performing nearest neighbor search on all individual points. This segmentation reduces the computational complexity from O(N) to O(M) where M is the number of local regions, significantly decreasing processing time while maintaining acceptable image quality.

The patent uses representative values (copies) of local regions instead of the actual individual point data. By calculating filtering results based on these simplified representative values and then applying them back to the original points, the system achieves similar filtering effects with much lower computational cost, avoiding the need for exhaustive nearest neighbor searches.

The patent segments the point cloud into local regions and performs filtering operations on representative values of these regions rather than individual points. This maintains sufficient filter accuracy for visual purposes while dramatically improving processing efficiency by reducing the number of operations from proportional to total point count to proportional to number of regions.

The patent applies filtering based on representative values which is a partial action compared to full nearest neighbor search on all points. This partial action is sufficient to achieve the desired filtering effect for visualization purposes, and the patent explicitly states that excessive precision is not always necessary for acceptable results.