Abstract:

To reduce storage and computational cost for processing and visualizing large-scale 3D point clouds, an efficient resampling strategy is needed to select a representative subset of 3D points that can preserve contours in the original 3D point cloud. We tackle this problem by using graph-based techniques as graphs can represent underlying surfaces and lend themselves well to efficient computation. We first construct a general graph for a 3D point cloud and then propose a graphbased metric to quantify the contour information via highpass graph filtering. Finally, we obtain an optimal resampling distribution that preserves the contour information by solving an optimization problem. When browsing, the proposed graph-based resampling performs better than uniform resampling both for toy point clouds as well as real large-scale point clouds. Furthermore, as neither mesh construction nor surface normal calculation is involved, the proposed graph-based method is computationally more efficient than the mesh-based methods.