Adaptive feature-conserving compression for large scale point clouds

In this work, we introduce a practical method for reducing big point clouds of buildings and infrastructure. The proposed method introduces bilateral filtering with a tailored set of evaluation functions to conserve maximum information. The statistical parameters necessary for our model are selected by examining various point properties of a comprehensive dataset. The dataset contains artificial, photogrammetric and laser-scanned point clouds and has been made publicly available. For verification, we showcase our filtering method by preserving more information than voxel grid or density filters, enabling even sparser photogrammetric datasets. Finally, we discuss some encoding strategies as well as the best balance between size and resolution.