Adaptive out-of-core simplification of large point clouds

With the increasing of data complexity, the needs for out-of-core simplification become evident. However, most of the existing point-based simplification algorithms adopt in-core scheme. We present an adaptive out-of-core algorithm for simplifying point-sampled models. Our approach uses quadric matrix to analyze the detailed regions of the initial simplified model that generated by an out-of-core uniform clustering. And then we use point-pair contraction to further simplify the flat regions and point-split to refine the detailed regions. Since the algorithm is input insensitive, it obtains high quality with low memory requirement.